Oracle Reports

Oracle Reports is a tool for developing reports against data stored in an Oracle database. Oracle Reports consists of Oracle Reports Developer (a component of the Oracle Developer Suite) and Oracle Application Server Reports Services (a component of the Oracle Application Server).
Output formats.
The report output can be delivered directly to a printer or saved in the following formats: HTML, RTF, PDF, XML, Microsoft Excel
History.
Oracle RPT.
Oracle RPT was an early, primitive predecessor to SQL*Report Writer. There was no editor or IDE provided and instead the reports were created by editing text files to control the report output.
Oracle Reports 2.5.
Release April 1995.
New Object Navigator.
New Toolbars.
New Menus.
Still no undos.
More stable IDE.
Oracle Reports 6i.
New features added in 6i:
Oracle Reports 9i.
New features added in 9i:
Oracle Reports 10g.
New features added in 10g:


Knuckle boom crane

A knuckle boom crane is a kind of standard crane whose boom articulates at the 'knuckle' near the middle, letting it fold back like a finger. This provides a compact size for storage and manoeuvring.
Knuckle boom cranes have become very common on offshore vessels as less of the deck space is blocked by the crane. Disadvantages of this crane type are the higher power demand and increased maintenance requirement due to the increased number of moving parts.
Knuckle boom crane arms are much lighter than boom truck cranes, and they are designed to allow for more payloads to be carried on the back of the truck that it is mounted on. The majority of them are mounted behind the cab and leave the entire bed of the truck empty.
The cranes come with different types of control systems, such as: stand up, control from the ground, seat control, or radio remote control. The radio remote systems now can start the crane as well as run the crane. Currently, they come equipped with a computer readout system that immediately gives readouts from the system if the crane is overloaded or not.


Electrical discharge machining

Electrical discharge machining (EDM), also known as spark machining, spark eroding, die sinking, wire burning or wire erosion, is a metal
fabrication process whereby a desired shape is obtained by using electrical discharges (sparks). Material is removed from the work piece by a series of rapidly recurring current discharges between two electrodes, separated by a dielectric liquid and subject to an electric voltage. One of the electrodes is called the tool-electrode, or simply the or , while the other is called the workpiece-electrode, or . The process depends upon the tool and work piece not making physical contact.
When the voltage between the two electrodes is increased, the intensity of the electric field in the volume between the electrodes becomes greater, causing dielectric break down of the liquid, and produces an electric arc. As a result, material is removed from the electrodes. Once the current stops (or is stopped, depending on the type of generator), new liquid dielectric is conveyed into the inter-electrode volume, enabling the solid particles (debris) to be carried away and the insulating properties of the dielectric to be restored. Adding new liquid dielectric in the inter-electrode volume is commonly referred to as . After a current flow, the voltage between the electrodes is restored to what it was before the breakdown, so that a new liquid dielectric breakdown can occur to repeat the cycle.
History.
The erosive effect of electrical discharges was first noted in 1770 by English physicist Joseph Priestley.
Die-sink EDM.
Two Soviet scientists, B. R. Lazarenko and N. I. Lazarenko, were tasked in 1943 to investigate ways of preventing the erosion of tungsten electrical contacts due to sparking. They failed in this task but found that the erosion was more precisely controlled if the electrodes were immersed in a dielectric fluid. This led them to invent an EDM machine used for working difficult-to-machine materials such as tungsten. The Lazarenkos' machine is known as an R-C-type machine, after the resistor–capacitor circuit (RC circuit) used to charge the electrodes.
Simultaneously but independently, an American team, Harold Stark, Victor Harding, and Jack Beaver, developed an EDM machine for removing broken drills and taps from aluminium castings. Initially constructing their machines from under-powered electric-etching tools, they were not very successful. But more powerful sparking units, combined with automatic spark repetition and fluid replacement with an electromagnetic interrupter arrangement produced practical machines. Stark, Harding, and Beaver's machines were able to produce 60 sparks per second. Later machines based on their design used vacuum tube circuits that were able to produce thousands of sparks per second, significantly increasing the speed of cutting.
Wire-cut EDM.
The wire-cut type of machine arose in the 1960s for making tools (dies) from hardened steel. The tool electrode in wire EDM is simply a wire. To avoid the erosion of the wire causing it to break, the wire is wound between two spools so that the active part of the wire is constantly changing. The earliest numerical controlled (NC) machines were conversions of punched-tape vertical milling machines. The first commercially available NC machine built as a wire-cut EDM machine was manufactured in the USSR in 1967. Machines that could optically follow lines on a master drawing were developed by David H. Dulebohn's group in the 1960s at Andrew Engineering Company for milling and grinding machines. Master drawings were later produced by computer numerical controlled (CNC) plotters for greater accuracy. A wire-cut EDM machine using the CNC drawing plotter and optical line follower techniques was produced in 1974. Dulebohn later used the same plotter CNC program to directly control the EDM machine, and the first CNC EDM machine was produced in 1976.
Commercial wire EDM capability and use has advanced substantially during recent decades. Feed rates have increased and surface finish can be finely controlled.
Generalities.
Electrical discharge machining is a machining method primarily used for hard metals or those that would be very difficult to machine with traditional techniques. EDM typically works with materials that are electrically conductive, although methods have also been proposed for using EDM to machine insulating ceramics. EDM can cut intricate contours or cavities in pre-hardened steel without the need for heat treatment to soften and re-harden them. This method can be used with any other metal or metal alloy such as titanium, hastelloy, kovar, and inconel. Also, applications of this process to shape polycrystalline diamond tools have been reported.
EDM is often included in the "non-traditional" or "non-conventional" group of machining methods together with processes such as electrochemical machining (ECM), water jet cutting (WJ, AWJ), laser cutting and opposite to the "conventional" group (turning, milling, grinding, drilling and any other process whose material removal mechanism is essentially based on mechanical forces).
Ideally, EDM can be seen as a series of breakdown and restoration of the liquid dielectric in-between the electrodes. However, caution should be exerted in considering such a statement because it is an idealized model of the process, introduced to describe the fundamental ideas underlying the process. Yet, any practical application involves many aspects that may also need to be considered. For instance, the removal of the debris from the inter-electrode volume is likely to be always partial. Thus the electrical properties of the dielectric in the inter-electrodes volume can be different from their nominal values and can even vary with time. The inter-electrode distance, often also referred to as spark-gap, is the result of the control algorithms of the specific machine used. The control of such a distance appears logically to be central to this process. Also, not all of the current between the dielectric is of the ideal type described above: the spark-gap can be short-circuited by the debris. The control system of the electrode may fail to react quickly enough to prevent the two electrodes (tool and workpiece) from coming into contact, with a consequent short circuit. This is unwanted because a short circuit contributes to material removal differently from the ideal case. The flushing action can be inadequate to restore the insulating properties of the dielectric so that the current always happens in the point of the inter-electrode volume (this is referred to as arcing), with a consequent unwanted change of shape (damage) of the tool-electrode and workpiece. Ultimately, a description of this process in a suitable way for the specific purpose at hand is what makes the EDM area such a rich field for further investigation and research.
To obtain a specific geometry, the EDM tool is guided along the desired path very close to the work; ideally it should not touch the workpiece, although in reality this may happen due to the performance of the specific motion control in use. In this way, a large number of current discharges (colloquially also called sparks) happen, each contributing to the removal of material from both tool and workpiece, where small craters are formed. The size of the craters is a function of the technological parameters set for the specific job at hand. They can be with typical dimensions ranging from the nanoscale (in micro-EDM operations) to some hundreds of micrometers in roughing conditions.
The presence of these small craters on the tool results in the gradual erosion of the electrode. This erosion of the tool-electrode is also referred to as wear. Strategies are needed to counteract the detrimental effect of the wear on the geometry of the workpiece. One possibility is that of continuously replacing the tool-electrode during a machining operation. This is what happens if a continuously replaced wire is used as electrode. In this case, the correspondent EDM process is also called wire EDM. The tool-electrode can also be used in such a way that only a small portion of it is actually engaged in the machining process and this portion is changed on a regular basis. This is, for instance, the case when using a rotating disk as a tool-electrode. The corresponding process is often also referred to as EDM grinding.
A further strategy consists in using a set of electrodes with different sizes and shapes during the same EDM operation. This is often referred to as multiple electrode strategy, and is most common when the tool electrode replicates in negative the wanted shape and is advanced towards the blank along a single direction, usually the vertical direction (i.e. z-axis). This resembles the sink of the tool into the dielectric liquid in which the workpiece is immersed, so, not surprisingly, it is often referred to as die-sinking EDM (also called conventional EDM and ram EDM). The corresponding machines are often called sinker EDM. Usually, the electrodes of this type have quite complex forms. If the final geometry is obtained using a usually simple-shaped electrode which is moved along several directions and is possibly also subject to rotations, often the term EDM milling is used.
In any case, the severity of the wear is strictly dependent on the technological parameters used in the operation (for instance: polarity, maximum current, open circuit voltage). For example, in micro-EDM, also known as μ-EDM, these parameters are usually set at values which generates severe wear. Therefore, wear is a major problem in that area.
The problem of wear to graphite electrodes is being addressed. In one approach, a digital generator, controllable within milliseconds, reverses polarity as electro-erosion takes place. That produces an effect similar to electroplating that continuously deposits the eroded graphite back on the electrode. In another method, a so-called "Zero Wear" circuit reduces how often the discharge starts and stops, keeping it on for as long a time as possible.
Definition of the technological parameters.
Difficulties have been encountered in the definition of the technological parameters that drive the process.
Two broad categories of generators, also known as power supplies, are in use on EDM machines commercially available: the group based on RC circuits and the group based on transistor controlled pulses.
In both categories, the primary parameters at setup are the current and frequency delivered. In RC circuits, however, little control is expected over the time duration of the discharge, which is likely to depend on the actual spark-gap conditions (size and pollution) at the moment of the discharge. Also, the open circuit voltage (i.e. the voltage between the electrodes when the dielectric is not yet broken) can be identified as steady state voltage of the RC circuit.
In generators based on transistor control, the user is usually able to deliver a train of pulses of voltage to the electrodes. Each pulse can be controlled in shape, for instance, quasi-rectangular. In particular, the time between two consecutive pulses and the duration of each pulse can be set. The amplitude of each pulse constitutes the open circuit voltage. Thus, the maximum duration of discharge is equal to the duration of a pulse of voltage in the train. Two pulses of current are then expected not to occur for a duration equal or larger than the time interval between two consecutive pulses of voltage.
The maximum current during a discharge that the generator delivers can also be controlled. Because other sorts of generators may also be used by different machine builders, the parameters that may actually be set on a particular machine will depend on the generator manufacturer. The details of the generators and control systems on their machines are not always easily available to their user. This is a barrier to describing unequivocally the technological parameters of the EDM process. Moreover, the parameters affecting the phenomena occurring between tool and electrode are also related to the controller of the motion of the electrodes.
A framework to define and measure the electrical parameters during an EDM operation directly on inter-electrode volume with an oscilloscope external to the machine has been recently proposed by Ferri "et al." These authors conducted their research in the field of μ-EDM, but the same approach can be used in any EDM operation. This would enable the user to estimate directly the electrical parameters that affect their operations without relying upon machine manufacturer's claims. When machining different materials in the same setup conditions, the actual electrical parameters of the process are significantly different.
Material removal mechanism.
The first serious attempt at providing a physical explanation of the material removal during electric discharge machining is perhaps that of Van Dijck. Van Dijck presented a thermal model together with a computational simulation to explain the phenomena between the electrodes during electric discharge machining. However, as Van Dijck himself admitted in his study, the number of assumptions made to overcome the lack of experimental data at that time was quite significant.
Further models of what occurs during electric discharge machining in terms of heat transfer were developed in the late eighties and early nineties. It resulted in three scholarly papers: the first presenting a thermal model of material removal on the cathode, the second presenting a thermal model for the erosion occurring on the anode and the third introducing a model describing the plasma channel formed during the passage of the discharge current through the dielectric liquid. Validation of these models is supported by experimental data provided by AGIE.
These models give the most authoritative support for the claim that EDM is a thermal process, removing material from the two electrodes because of melting or vaporization, along with pressure dynamics established in the spark-gap by the collapsing of the plasma channel. However, for small discharge energies the models are inadequate to explain the experimental data. All these models hinge on a number of assumptions from such disparate research areas as submarine explosions, discharges in gases, and failure of transformers, so it is not surprising that alternative models have been proposed more recently in the literature trying to explain the EDM process.
Among these, the model from Singh and Ghosh reconnects the removal of material from the electrode to the presence of an electrical force on the surface of the electrode that could mechanically remove material and create the craters. This would be possible because the material on the surface has altered mechanical properties due to an increased temperature caused by the passage of electric current. The authors' simulations showed how they might explain EDM better than a thermal model (melting or evaporation), especially for small discharge energies, which are typically used in μ-EDM and in finishing operations.
Given the many available models, it appears that the material removal mechanism in EDM is not yet well understood and that further investigation is necessary to clarify it, especially considering the lack of experimental scientific evidence to build and validate the current EDM models. This explains an increased current research effort in related experimental techniques.
In this conclusion, there are following major factors are achieved during machining operations:
Types.
Sinker EDM.
Sinker EDM, also called ram EDM, cavity type EDM or volume EDM, consists of an electrode and workpiece submerged in an insulating liquid such as, more typically, oil or, less frequently, other dielectric fluids. The electrode and workpiece are connected to a suitable power supply. The power supply generates an electrical potential between the two parts. As the electrode approaches the workpiece, dielectric breakdown occurs in the fluid, forming a plasma channel, and a small spark jumps.
These sparks usually strike one at a time, because it is very unlikely that different locations in the inter-electrode space have the identical local electrical characteristics which would enable a spark to occur simultaneously in all such locations. These sparks happen in huge numbers at seemingly random locations between the electrode and the workpiece. As the base metal is eroded, and the spark gap subsequently increased, the electrode is lowered automatically by the machine so that the process can continue uninterrupted. Several hundred thousand sparks occur per second, with the actual duty cycle carefully controlled by the setup parameters. These controlling cycles are sometimes known as "on time" and "off time", which are more formally defined in the literature.
The on time setting determines the length or duration of the spark. Hence, a longer on time produces a deeper cavity from each spark, creating a rougher finish on the workpiece. The reverse is true for a shorter on time. Off time is the period of time between sparks. Although not directly affecting the machining of the part, the off time allows the flushing of dielectric fluid through a nozzle to clean out the eroded debris. Insufficient debris removal can cause repeated strikes in the same location which can lead to a short circuit. Modern controllers monitor the characteristics of the arcs and can alter parameters in microseconds to compensate. The typical part geometry is a complex 3D shape, often with small or odd shaped angles. Vertical, orbital, vectorial, directional, helical, conical, rotational, spin and indexing machining cycles are also used.
Wire EDM.
In "wire electrical discharge machining" (WEDM), also known as "wire-cut EDM" and "wire cutting", a thin single-strand metal wire, usually brass, is fed through the workpiece, submerged in a tank of dielectric fluid, typically deionized water. Wire-cut EDM is typically used to cut plates as thick as 300mm and to make punches, tools, and dies from hard metals that are difficult to machine with other methods.
The wire, which is constantly fed from a spool, is held between upper and lower diamond guides which is centered in a water nozzle head. The guides, usually CNC-controlled, move in the "x"–"y" plane. On most machines, the upper guide can also move independently in the "z"–"u"–"v" axis, giving rise to the ability to cut tapered and transitioning shapes (circle on the bottom, square at the top for example). The upper guide can control axis movements in the GCode standard, "x"–"y"–"u"–"v"–"i"–"j"–"k"–"l"–. This allows the wire-cut EDM to be programmed to cut very intricate and delicate shapes.
The upper and lower diamond guides are usually accurate to , and can have a cutting path or "kerf" as small as using Ø wire, though the average cutting kerf that achieves the best economic cost and machining time is using Ø brass wire. The reason that the cutting width is greater than the width of the wire is because sparking occurs from the sides of the wire to the work piece, causing erosion. This "overcut" is necessary, for many applications it is adequately predictable and therefore can be compensated for (for instance in micro-EDM this is not often the case). Spools of wire are long — an 8 kg spool of 0.25 mm wire is just over 19 kilometers in length. Wire diameter can be as small as and the geometry precision is not far from ± .
The wire-cut process uses water as its dielectric fluid, controlling its resistivity and other electrical properties with filters and PID controlled de-ionizer units. The water flushes the cut debris away from the cutting zone. Flushing is an important factor in determining the maximum feed rate for a given material thickness.
Along with tighter tolerances, multi axis EDM wire-cutting machining centers have added features such as multi heads for cutting two parts at the same time, controls for preventing wire breakage, automatic self-threading features in case of wire breakage, and programmable machining strategies to optimize the operation.
Wire-cutting EDM is commonly used when low residual stresses are desired, because it does not require high cutting forces for removal of material. If the energy/power per pulse is relatively low (as in finishing operations), little change in the mechanical properties of a material is expected due to these low residual stresses, although material that hasn't been stress-relieved can distort in the machining process.
The work piece may undergo a significant thermal cycle, its severity depending on the technological parameters used. Such thermal cycles may cause formation of a recast layer on the part and residual tensile stresses on the work piece. If machining takes place after heat treatment, dimensional accuracy will not be affected by heat treat distortion.
Fast hole drilling EDM.
Fast hole drilling EDM was designed for producing fast, accurate, small, and deep holes. It is conceptually akin to sinker EDM but the electrode is a rotating tube conveying a pressurized jet of dielectric fluid. It can make a hole an inch deep in about a minute and is a good way to machine holes in materials too hard for twist-drill machining. This EDM drilling type is used largely in the aerospace industry, producing cooling holes into aero blades and other components. It is also used to drill holes in industrial gas turbine blades, in molds and dies, and in bearings.
Applications.
Prototype production.
The EDM process is most widely used by the mold-making, tool, and die industries, but is becoming a common method of making prototype and production parts, especially in the aerospace, automobile and electronics industries in which production quantities are relatively low. In sinker EDM, a graphite, copper tungsten, or pure copper electrode is machined into the desired (negative) shape and fed into the workpiece on the end of a vertical ram.
Coinage die making.
For the creation of dies for producing jewelry and badges, or blanking and piercing (through use of a pancake die) by the coinage (stamping) process, the positive master may be made from sterling silver, since (with appropriate machine settings) the master is significantly eroded and is used only once. The resultant negative die is then hardened and used in a drop hammer to produce stamped flats from cutout sheet blanks of bronze, silver, or low proof gold alloy. For badges these flats may be further shaped to a curved surface by another die. This type of EDM is usually performed submerged in an oil-based dielectric. The finished object may be further refined by hard (glass) or soft (paint) enameling, or electroplated with pure gold or nickel. Softer materials such as silver may be hand engraved as a refinement.
Small hole drilling.
Small hole drilling EDM is used in a variety of applications.
On wire-cut EDM machines, small hole drilling EDM is used to make a through hole in a workpiece through which to thread the wire for the wire-cut EDM operation. A separate EDM head specifically for small hole drilling is mounted on a wire-cut machine and allows large hardened plates to have finished parts eroded from them as needed and without pre-drilling.
Small hole EDM is used to drill rows of holes into the leading and trailing edges of turbine blades used in jet engines. Gas flow through these small holes allows the engines to use higher temperatures than otherwise possible. The high-temperature, very hard, single crystal alloys employed in these blades makes conventional machining of these holes with high aspect ratio extremely difficult, if not impossible.
Small hole EDM is also used to create microscopic orifices for fuel system components, spinnerets for synthetic fibers such as rayon, and other applications.
There are also stand-alone small hole drilling EDM machines with an "x"–"y" axis also known as a super drill or "hole popper" that can machine blind or through holes. EDM drills bore holes with a long brass or copper tube electrode that rotates in a chuck with a constant flow of distilled or deionized water flowing through the electrode as a flushing agent and dielectric. The electrode tubes operate like the wire in wire-cut EDM machines, having a spark gap and wear rate. Some small-hole drilling EDMs are able to drill through 100 mm of soft or hardened steel in less than 10 seconds, averaging 50% to 80% wear rate. Holes of 0.3 mm to 6.1 mm can be achieved in this drilling operation. Brass electrodes are easier to machine but are not recommended for wire-cut operations due to eroded brass particles causing "brass on brass" wire breakage, therefore copper is recommended.
Metal disintegration machining.
Several manufacturers produce EDM machines for the specific purpose of removing broken cutting tools and fasteners from work pieces. In this application, the process is termed "metal disintegration machining" or MDM. The metal disintegration process removes only the center of the broken tool or fastener, leaving the hole intact and allowing a part to be reclaimed.
Closed loop manufacturing.
Closed loop manufacturing can improve the accuracy and reduce the tool costs
Advantages and disadvantages.
EDM is often compared to Electrochemical Machining.
Advantages of EDM include:
Disadvantages of EDM include:


Ministry of Instrumentation, Automation and Control Systems

The Ministry of Instrument-Making, Automation Devices and Control Systems (Minpribor; ) was a government ministry in the Soviet Union.
Established in 1959 as State Committee for Automation and Machine Building; it assumed its ministerial title in 1965; oversees development and integration into industry of automated control systems. The ministry developed and manufactured systems for industrial control, planning and management.
List of ministers.
"Source":


Control engineering

Control engineering or control systems engineering is an engineering discipline that deals with control systems, applying control theory to design equipment and systems with desired behaviors in control environments. The discipline of controls overlaps and is usually taught along with electrical engineering and mechanical engineering at many institutions around the world.
The practice uses sensors and detectors to measure the output performance of the process being controlled; these measurements are used to provide corrective feedback helping to achieve the desired performance. Systems designed to perform without requiring human input are called automatic control systems (such as cruise control for regulating the speed of a car). Multi-disciplinary in nature, control systems engineering activities focus on implementation of control systems mainly derived by mathematical modeling of a diverse range of systems.
Overview.
Modern day control engineering is a relatively new field of study that gained significant attention during the 20th century with the advancement of technology. It can be broadly defined or classified as practical application of control theory. Control engineering plays an essential role in a wide range of control systems, from simple household washing machines to high-performance fighter aircraft. It seeks to understand physical systems, using mathematical modelling, in terms of inputs, outputs and various components with different behaviors; to use control system design tools to develop controllers for those systems; and to implement controllers in physical systems employing available technology. A system can be mechanical, electrical, fluid, chemical, financial or biological, and its mathematical modelling, analysis and controller design uses control theory in one or many of the time, frequency and complex-s domains, depending on the nature of the design problem.
Control engineering is the engineering discipline that focuses on the modeling of a diverse range of dynamic systems (e.g. mechanical systems) and the design of controllers that will cause these systems to behave in the desired manner. Although such controllers need not be electrical, many are and hence control engineering is often viewed as a subfield of electrical engineering.
Electrical circuits, digital signal processors and microcontrollers can all be used to implement control systems. Control engineering has a wide range of applications from the flight and propulsion systems of commercial airliners to the cruise control present in many modern automobiles.
In most cases, control engineers utilize feedback when designing control systems. This is often accomplished using a PID controller system. For example, in an automobile with cruise control the vehicle's speed is continuously monitored and fed back to the system, which adjusts the motor's torque accordingly. Where there is regular feedback, control theory can be used to determine how the system responds to such feedback. In practically all such systems stability is important and control theory can help ensure stability is achieved.
Although feedback is an important aspect of control engineering, control engineers may also work on the control of systems without feedback. This is known as open loop control. A classic example of open loop control is a washing machine that runs through a pre-determined cycle without the use of sensors.
History.
Automatic control systems were first developed over two thousand years ago. The first feedback control device on record is thought to be the ancient Ktesibios's water clock in Alexandria, Egypt, around the third century BCE. It kept time by regulating the water level in a vessel and, therefore, the water flow from that vessel. This certainly was a successful device as water clocks of similar design were still being made in Baghdad when the Mongols captured the city in 1258 CE. A variety of automatic devices have been used over the centuries to accomplish useful tasks or simply just to entertain. The latter includes the automata, popular in Europe in the 17th and 18th centuries, featuring dancing figures that would repeat the same task over and over again; these automata are examples of open-loop control. Milestones among feedback, or "closed-loop" automatic control devices, include the temperature regulator of a furnace attributed to Drebbel, circa 1620, and the centrifugal flyball governor used for regulating the speed of steam engines by James Watt in 1788.
In his 1868 paper "On Governors", James Clerk Maxwell was able to explain instabilities exhibited by the flyball governor using differential equations to describe the control system. This demonstrated the importance and usefulness of mathematical models and methods in understanding complex phenomena, and it signaled the beginning of mathematical control and systems theory. Elements of control theory had appeared earlier but not as dramatically and convincingly as in Maxwell's analysis.
Control theory made significant strides over the next century. New mathematical techniques, as well as advances in electronic and computer technologies, made it possible to control significantly more complex dynamical systems than the original flyball governor could stabilize. New mathematical techniques included developments in optimal control in the 1950s and 1960s followed by progress in stochastic, robust, adaptive, nonlinear control methods in the 1970s and 1980s. Applications of control methodology have helped to make possible space travel and communication satellites, safer and more efficient aircraft, cleaner automobile engines, and cleaner and more efficient chemical processes.
Before it emerged as a unique discipline, control engineering was practiced as a part of mechanical engineering and control theory was studied as a part of electrical engineering since electrical circuits can often be easily described using control theory techniques. In the very first control relationships, a current output was represented by a voltage control input. However, not having adequate technology to implement electrical control systems, designers were left with the option of less efficient and slow responding mechanical systems. A very effective mechanical controller that is still widely used in some hydro plants is the governor. Later on, previous to modern power electronics, process control systems for industrial applications were devised by mechanical engineers using pneumatic and hydraulic control devices, many of which are still in use today.
Education.
At many universities around the world, control engineering courses are taught primarily in electrical engineering and mechanical engineering, but some courses can be instructed in mechatronics engineering, and aerospace engineering. In others, control engineering is connected to computer science, as most control techniques today are implemented through computers, often as embedded systems (as in the automotive field). The field of control within chemical engineering is often known as process control. It deals primarily with the control of variables in a chemical process in a plant. It is taught as part of the undergraduate curriculum of any chemical engineering program and employs many of the same principles in control engineering. Other engineering disciplines also overlap with control engineering as it can be applied to any system for which a suitable model can be derived. However, specialised control engineering departments do exist, for example, in Italy there are several master in Automation & Robotics that are fully specialised in Control engineering or the Department of Automatic Control and Systems Engineering at the University of Sheffield or the Department of Robotics and Control Engineering at the United States Naval Academy and the Department of Control and Automation Engineering at the Istanbul Technical University.
Control engineering has diversified applications that include science, finance management, and even human behavior. Students of control engineering may start with a linear control system course dealing with the time and complex-s domain, which requires a thorough background in elementary mathematics and Laplace transform, called classical control theory. In linear control, the student does frequency and time domain analysis. Digital control and nonlinear control courses require Z transformation and algebra respectively, and could be said to complete a basic control education.
Careers.
A control engineer's career starts with a bachelor's degree and can continue through the college process. Control engineer degrees are well paired with an electrical or mechanical engineering degree. Control engineers usually get jobs in technical managing where they typically lead interdisciplinary projects. There are many job opportunities in aerospace companies, manufacturing companies, automobile companies, power companies, and government agencies. Some places that hire Control Engineers include companies such as Rockwell Automation, NASA, Ford, and Goodrich. Control Engineers can possibly earn $66k annually from Lockheed Martin Corp. They can also earn up to $96k annually from General Motors Corporation.
According to a "Control Engineering" survey, most of the people who answered were control engineers in various forms of their own career. There are not very many careers that are classified as "control engineer," most of them are specific careers that have a small semblance to the overarching career of control engineering. A majority of the control engineers that took the survey in 2019 are system or product designers, or even control or instrument engineers. Most of the jobs involve process engineering or production or even maintenance, they are some variation of control engineering.
Recent advancement.
Originally, control engineering was all about continuous systems. Development of computer control tools posed a requirement of discrete control system engineering because the communications between the computer-based digital controller and the physical system are governed by a computer clock. The equivalent to Laplace transform in the discrete domain is the Z-transform. Today, many of the control systems are computer controlled and they consist of both digital and analog components.
Therefore, at the design stage either digital components are mapped into the continuous domain and the design is carried out in the continuous domain, or analog components are mapped into discrete domain and design is carried out there. The first of these two methods is more commonly encountered in practice because many industrial systems have many continuous systems components, including mechanical, fluid, biological and analog electrical components, with a few digital controllers.
Similarly, the design technique has progressed from paper-and-ruler based manual design to computer-aided design and now to computer-automated design or CAD which has been made possible by evolutionary computation. CAD can be applied not just to tuning a predefined control scheme, but also to controller structure optimisation, system identification and invention of novel control systems, based purely upon a performance requirement, independent of any specific control scheme.
Resilient control systems extend the traditional focus of addressing only planned disturbances to frameworks and attempt to address multiple types of unexpected disturbance; in particular, adapting and transforming behaviors of the control system in response to malicious actors, abnormal failure modes, undesirable human action, etc.


Control theory

Control theory is a field of control engineering and applied mathematics that deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any "delay", "overshoot", or "steady-state error" and ensuring a level of control stability; often with the aim to achieve a degree of optimality.
To do this, a controller with the requisite corrective behavior is required. This controller monitors the controlled process variable (PV), and compares it with the reference or set point (SP). The difference between actual and desired value of the process variable, called the "error" signal, or SP-PV error, is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Other aspects which are also studied are controllability and observability. Control theory is used in control system engineering to design automation that have revolutionized manufacturing, aircraft, communications and other industries, and created new fields such as robotics.
Extensive use is usually made of a diagrammatic style known as the block diagram. In it the transfer function, also known as the system function or network function, is a mathematical model of the relation between the input and output based on the differential equations describing the system.
Control theory dates from the 19th century, when the theoretical basis for the operation of governors was first described by James Clerk Maxwell. Control theory was further advanced by Edward Routh in 1874, Charles Sturm and in 1895, Adolf Hurwitz, who all contributed to the establishment of control stability criteria; and from 1922 onwards, the development of PID control theory by Nicolas Minorsky.
Although a major application of mathematical control theory is in control systems engineering, which deals with the design of process control systems for industry, other applications range far beyond this. As the general theory of feedback systems, control theory is useful wherever feedback occurs - thus control theory also has applications in life sciences, computer engineering, sociology and operations research.
History.
Although control systems of various types date back to antiquity, a more formal analysis of the field began with a dynamics analysis of the centrifugal governor, conducted by the physicist James Clerk Maxwell in 1868, entitled "On Governors". A centrifugal governor was already used to regulate the velocity of windmills. Maxwell described and analyzed the phenomenon of self-oscillation, in which lags in the system may lead to overcompensation and unstable behavior. This generated a flurry of interest in the topic, during which Maxwell's classmate, Edward John Routh, abstracted Maxwell's results for the general class of linear systems. Independently, Adolf Hurwitz analyzed system stability using differential equations in 1877, resulting in what is now known as the Routh–Hurwitz theorem.
A notable application of dynamic control was in the area of crewed flight. The Wright brothers made their first successful test flights on December 17, 1903, and were distinguished by their ability to control their flights for substantial periods (more so than the ability to produce lift from an airfoil, which was known). Continuous, reliable control of the airplane was necessary for flights lasting longer than a few seconds.
By World War II, control theory was becoming an important area of research. Irmgard Flügge-Lotz developed the theory of discontinuous automatic control systems, and applied the bang-bang principle to the development of automatic flight control equipment for aircraft. Other areas of application for discontinuous controls included fire-control systems, guidance systems and electronics.
Sometimes, mechanical methods are used to improve the stability of systems. For example, ship stabilizers are fins mounted beneath the waterline and emerging laterally. In contemporary vessels, they may be gyroscopically controlled active fins, which have the capacity to change their angle of attack to counteract roll caused by wind or waves acting on the ship.
The Space Race also depended on accurate spacecraft control, and control theory has also seen an increasing use in fields such as economics and artificial intelligence. Here, one might say that the goal is to find an internal model that obeys the good regulator theorem. So, for example, in economics, the more accurately a (stock or commodities) trading model represents the actions of the market, the more easily it can control that market (and extract "useful work" (profits) from it). In AI, an example might be a chatbot modelling the discourse state of humans: the more accurately it can model the human state (e.g. on a telephone voice-support hotline), the better it can manipulate the human (e.g. into performing the corrective actions to resolve the problem that caused the phone call to the help-line). These last two examples take the narrow historical interpretation of control theory as a set of differential equations modeling and regulating kinetic motion, and broaden it into a vast generalization of a regulator interacting with a plant.
Linear and nonlinear control theory.
The field of control theory can be divided into two branches:
Analysis techniques - frequency domain and time domain.
Mathematical techniques for analyzing and designing control systems fall into two different categories:
In contrast to the frequency domain analysis of the classical control theory, modern control theory utilizes the time-domain state space representation, a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations. To abstract from the number of inputs, outputs, and states, the variables are expressed as vectors and the differential and algebraic equations are written in matrix form (the latter only being possible when the dynamical system is linear). The state space representation (also known as the "time-domain approach") provides a convenient and compact way to model and analyze systems with multiple inputs and outputs. With inputs and outputs, we would otherwise have to write down Laplace transforms to encode all the information about a system. Unlike the frequency domain approach, the use of the state-space representation is not limited to systems with linear components and zero initial conditions. "State space" refers to the space whose axes are the state variables. The state of the system can be represented as a point within that space.
System interfacing - SISO & MIMO.
Control systems can be divided into different categories depending on the number of inputs and outputs.
Classical SISO System Design.
The scope of classical control theory is limited to single-input and single-output (SISO) system design, except when analyzing for disturbance rejection using a second input. The system analysis is carried out in the time domain using differential equations, in the complex-s domain with the Laplace transform, or in the frequency domain by transforming from the complex-s domain. Many systems may be assumed to have a second order and single variable system response in the time domain. A controller designed using classical theory often requires on-site tuning due to incorrect design approximations. Yet, due to the easier physical implementation of classical controller designs as compared to systems designed using modern control theory, these controllers are preferred in most industrial applications. The most common controllers designed using classical control theory are PID controllers. A less common implementation may include either or both a Lead or Lag filter. The ultimate end goal is to meet requirements typically provided in the time-domain called the step response, or at times in the frequency domain called the open-loop response. The step response characteristics applied in a specification are typically percent overshoot, settling time, etc. The open-loop response characteristics applied in a specification are typically Gain and Phase margin and bandwidth. These characteristics may be evaluated through simulation including a dynamic model of the system under control coupled with the compensation model.
Modern MIMO System Design.
Modern control theory is carried out in the state space, and can deal with multiple-input and multiple-output (MIMO) systems. This overcomes the limitations of classical control theory in more sophisticated design problems, such as fighter aircraft control, with the limitation that no frequency domain analysis is possible. In modern design, a system is represented to the greatest advantage as a set of decoupled first order differential equations defined using state variables. Nonlinear, multivariable, adaptive and robust control theories come under this division. Matrix methods are significantly limited for MIMO systems where linear independence cannot be assured in the relationship between inputs and outputs . Being fairly new, modern control theory has many areas yet to be explored. Scholars like Rudolf E. Kálmán and Aleksandr Lyapunov are well known among the people who have shaped modern control theory.
Topics in control theory.
Stability.
The "stability" of a general dynamical system with no input can be described with Lyapunov stability criteria.
For simplicity, the following descriptions focus on continuous-time and discrete-time linear systems.
Mathematically, this means that for a causal linear system to be stable all of the poles of its transfer function must have negative-real values, i.e. the real part of each pole must be less than zero. Practically speaking, stability requires that the transfer function complex poles reside
The difference between the two cases is simply due to the traditional method of plotting continuous time versus discrete time transfer functions. The continuous Laplace transform is in Cartesian coordinates where the formula_1 axis is the real axis and the discrete Z-transform is in circular coordinates where the formula_2 axis is the real axis.
When the appropriate conditions above are satisfied a system is said to be asymptotically stable; the variables of an asymptotically stable control system always decrease from their initial value and do not show permanent oscillations. Permanent oscillations occur when a pole has a real part exactly equal to zero (in the continuous time case) or a modulus equal to one (in the discrete time case). If a simply stable system response neither decays nor grows over time, and has no oscillations, it is marginally stable; in this case the system transfer function has non-repeated poles at the complex plane origin (i.e. their real and complex component is zero in the continuous time case). Oscillations are present when poles with real part equal to zero have an imaginary part not equal to zero.
If a system in question has an impulse response of
then the Z-transform (see this example), is given by
which has a pole in formula_5 (zero imaginary part). This system is BIBO (asymptotically) stable since the pole is "inside" the unit circle.
However, if the impulse response was
then the Z-transform is
which has a pole at formula_8 and is not BIBO stable since the pole has a modulus strictly greater than one.
Numerous tools exist for the analysis of the poles of a system. These include graphical systems like the root locus, Bode plots or the Nyquist plots.
Mechanical changes can make equipment (and control systems) more stable. Sailors add ballast to improve the stability of ships. Cruise ships use antiroll fins that extend transversely from the side of the ship for perhaps 30 feet (10 m) and are continuously rotated about their axes to develop forces that oppose the roll.
Controllability and observability.
Controllability and observability are main issues in the analysis of a system before deciding the best control strategy to be applied, or whether it is even possible to control or stabilize the system. Controllability is related to the possibility of forcing the system into a particular state by using an appropriate control signal. If a state is not controllable, then no signal will ever be able to control the state. If a state is not controllable, but its dynamics are stable, then the state is termed "stabilizable". Observability instead is related to the possibility of "observing", through output measurements, the state of a system. If a state is not observable, the controller will never be able to determine the behavior of an unobservable state and hence cannot use it to stabilize the system. However, similar to the stabilizability condition above, if a state cannot be observed it might still be detectable.
From a geometrical point of view, looking at the states of each variable of the system to be controlled, every "bad" state of these variables must be controllable and observable to ensure a good behavior in the closed-loop system. That is, if one of the eigenvalues of the system is not both controllable and observable, this part of the dynamics will remain untouched in the closed-loop system. If such an eigenvalue is not stable, the dynamics of this eigenvalue will be present in the closed-loop system which therefore will be unstable. Unobservable poles are not present in the transfer function realization of a state-space representation, which is why sometimes the latter is preferred in dynamical systems analysis.
Solutions to problems of an uncontrollable or unobservable system include adding actuators and sensors.
Control specification.
Several different control strategies have been devised in the past years. These vary from extremely general ones (PID controller), to others devoted to very particular classes of systems (especially robotics or aircraft cruise control).
A control problem can have several specifications. Stability, of course, is always present. The controller must ensure that the closed-loop system is stable, regardless of the open-loop stability. A poor choice of controller can even worsen the stability of the open-loop system, which must normally be avoided. Sometimes it would be desired to obtain particular dynamics in the closed loop: i.e. that the poles have formula_9, where formula_10 is a fixed value strictly greater than zero, instead of simply asking that formula_11.
Another typical specification is the rejection of a step disturbance; including an integrator in the open-loop chain (i.e. directly before the system under control) easily achieves this. Other classes of disturbances need different types of sub-systems to be included.
Other "classical" control theory specifications regard the time-response of the closed-loop system. These include the rise time (the time needed by the control system to reach the desired value after a perturbation), peak overshoot (the highest value reached by the response before reaching the desired value) and others (settling time, quarter-decay). Frequency domain specifications are usually related to robustness (see after).
Modern performance assessments use some variation of integrated tracking error (IAE, ISA, CQI).
Model identification and robustness.
A control system must always have some robustness property. A robust controller is such that its properties do not change much if applied to a system slightly different from the mathematical one used for its synthesis. This requirement is important, as no real physical system truly behaves like the series of differential equations used to represent it mathematically. Typically a simpler mathematical model is chosen in order to simplify calculations, otherwise, the true system dynamics can be so complicated that a complete model is impossible.
The process of determining the equations that govern the model's dynamics is called system identification. This can be done off-line: for example, executing a series of measures from which to calculate an approximated mathematical model, typically its transfer function or matrix. Such identification from the output, however, cannot take account of unobservable dynamics. Sometimes the model is built directly starting from known physical equations, for example, in the case of a mass-spring-damper system we know that formula_12. Even assuming that a "complete" model is used in designing the controller, all the parameters included in these equations (called "nominal parameters") are never known with absolute precision; the control system will have to behave correctly even when connected to a physical system with true parameter values away from nominal.
Some advanced control techniques include an "on-line" identification process (see later). The parameters of the model are calculated ("identified") while the controller itself is running. In this way, if a drastic variation of the parameters ensues, for example, if the robot's arm releases a weight, the controller will adjust itself consequently in order to ensure the correct performance.
Analysis of the robustness of a SISO (single input single output) control system can be performed in the frequency domain, considering the system's transfer function and using Nyquist and Bode diagrams. Topics include gain and phase margin and amplitude margin. For MIMO (multi-input multi output) and, in general, more complicated control systems, one must consider the theoretical results devised for each control technique (see next section). I.e., if particular robustness qualities are needed, the engineer must shift their attention to a control technique by including these qualities in its properties.
A particular robustness issue is the requirement for a control system to perform properly in the presence of input and state constraints. In the physical world every signal is limited. It could happen that a controller will send control signals that cannot be followed by the physical system, for example, trying to rotate a valve at excessive speed. This can produce undesired behavior of the closed-loop system, or even damage or break actuators or other subsystems. Specific control techniques are available to solve the problem: model predictive control (see later), and anti-wind up systems. The latter consists of an additional control block that ensures that the control signal never exceeds a given threshold.
System classifications.
Linear systems control.
For MIMO systems, pole placement can be performed mathematically using a state space representation of the open-loop system and calculating a feedback matrix assigning poles in the desired positions. In complicated systems this can require computer-assisted calculation capabilities, and cannot always ensure robustness. Furthermore, all system states are not in general measured and so observers must be included and incorporated in pole placement design.
Nonlinear systems control.
Processes in industries like robotics and the aerospace industry typically have strong nonlinear dynamics. In control theory it is sometimes possible to linearize such classes of systems and apply linear techniques, but in many cases it can be necessary to devise from scratch theories permitting control of nonlinear systems. These, e.g., feedback linearization, backstepping, sliding mode control, trajectory linearization control normally take advantage of results based on Lyapunov's theory. Differential geometry has been widely used as a tool for generalizing well-known linear control concepts to the nonlinear case, as well as showing the subtleties that make it a more challenging problem. Control theory has also been used to decipher the neural mechanism that directs cognitive states.
Decentralized systems control.
When the system is controlled by multiple controllers, the problem is one of decentralized control. Decentralization is helpful in many ways, for instance, it helps control systems to operate over a larger geographical area. The agents in decentralized control systems can interact using communication channels and coordinate their actions.
Deterministic and stochastic systems control.
A stochastic control problem is one in which the evolution of the state variables is subjected to random shocks from outside the system. A deterministic control problem is not subject to external random shocks.
Main control strategies.
Every control system must guarantee first the stability of the closed-loop behavior. For linear systems, this can be obtained by directly placing the poles. Nonlinear control systems use specific theories (normally based on Aleksandr Lyapunov's Theory) to ensure stability without regard to the inner dynamics of the system. The possibility to fulfill different specifications varies from the model considered and the control strategy chosen.
People in systems and control.
Many active and historical figures made significant contribution to control theory including


Part program

The part program is a sequence of instructions, which describe the work, which has to be done on a part, in the form required by a computer under the control of computer numerical control (CNC) software. It is the task of preparing a program sheet from a drawing sheet. All data is fed into the CNC system using a standardized format. Programming is where all the machining data are compiled and where the data are translated into a language which can be understood by the control system of the machine tool.
The machining data is as follows :


Mathematical programming with equilibrium constraints (MPEC) is the study of
constrained optimization problems where the constraints include variational inequalities or complementarities. MPEC is related to the Stackelberg game.
MPEC is used in the study of engineering design, economic equilibrium, and multilevel games.
MPEC is difficult to deal with because its feasible region is not necessarily convex or even connected.


Turning

Turning is a machining process in which a cutting tool, typically a non-rotary tool bit, describes a helix toolpath by moving more or less linearly while the workpiece rotates.
Usually the term "turning" is reserved for the generation of "external" surfaces by this cutting action, whereas this same essential cutting action when applied to "internal" surfaces (holes, of one kind or another) is called "boring". Thus the phrase "turning and boring" categorizes the larger family of processes known as lathing. The cutting of faces on the workpiece, whether with a turning or boring tool, is called "facing", and may be lumped into either category as a subset.
Turning can be done manually, in a traditional form of lathe, which frequently requires continuous supervision by the operator, or by using an automated lathe which does not. Today the most common type of such automation is computer numerical control, better known as CNC. (CNC is also commonly used with many other types of machining besides turning.)
When turning, the workpiece (a piece of relatively rigid material such as wood, metal, plastic, or stone) is rotated and a cutting tool is traversed along 1, 2, or 3 axes of motion to produce precise diameters and depths. Turning can be either on the outside of the cylinder or on the inside (also known as boring) to produce tubular components to various geometries. Although now quite rare, early lathes could even be used to produce complex geometric figures, even the platonic solids; although since the advent of CNC it has become unusual to use non-computerized toolpath control for this purpose.
The turning processes are typically carried out on a lathe, considered to be the oldest of machine tools, and can be of different types such as "straight turning", "taper turning", "profiling" or "external grooving". Those types of turning processes can produce various shapes of materials such as "straight", "conical", "curved", or "grooved" workpieces.
In general, turning uses simple "single-point cutting" tools. Each group of workpiece materials has an optimum set of tool angles that have been developed through the years.
The bits of waste metal from turning operations are known as chips (North America), or swarf (Britain). In some areas they may be known as "turnings".
The tool's axes of movement may be literally a straight line, or they may be along some set of curves or angles, but they are essentially linear (in the non mathematical sense).
A component that is subject to turning operations can be termed as a “Turned Part” or “Machined Component”. Turning operations are carried out on a lathe machine which can be manually or CNC operated.
Turning operations.
Turning specific operations include:
The general process of turning involves rotating a part while a single-point cutting tool is moved parallel to the axis of rotation. Turning can be done on the external surface of the part as well as the internal surface (the process known as boring). The starting material is generally a workpiece generated by other processes such as casting, forging, extrusion, or drawing.
Facing in the context of turning work involves moving the cutting tool at right angles to the axis of rotation of the rotating workpiece. This can be performed by the operation of the cross-slide, if one is fitted, as distinct from the longitudinal feed (turning). It is frequently the first operation performed in the production of the workpiece, and often the last—hence the phrase "ending up".
This process, also called parting off or cutoff, is used to create deep grooves which will remove a completed or part-complete component from its parent stock.
Grooving is like parting, except that grooves are cut to a specific depth instead of severing a completed/part-complete component from the stock. Grooving can be performed on internal and external surfaces, as well as on the face of the part (face grooving or trepanning).
Non-specific operations include:
Lathes.
A lathe is a machine tool used principally for shaping pieces of metal, wood, or other materials by causing the workpiece to be held and rotated by the lathe while a tool bit is advanced into the work causing the cutting action. Lathes can be divided into three types for easy identification: engine lathe, turret lathe, and "special purpose lathes". Some smaller ones are bench mounted and semi-portable. The larger lathes are floor mounted and may require special transportation if they must be moved.
Field and maintenance shops generally use a lathe that can be adapted to many operations and that is not too large to be moved from one work site to another. The engine lathe is ideally suited for this purpose. A trained operator can accomplish more machining jobs with the engine lathe than with any other machine tool. Turret lathes and special purpose lathes are usually used in production or job shops for mass production or specialized parts, while basic engine lathes are usually used for any type of lathe work.
Tooling.
The various angles, shapes, and sizes of a "single-point cutting" tool have direct relation to the resulting surface of a workpiece in machining operations. Different types of angle such as "rake angle", "side rake angle", "cutting-edge angle", "relief angle", "nose radius" exist and may be different with respect to the workpiece. Also, there are many shapes of "single-point cutting" tools, such as "V-shaped" and "Square." Usually, a special toolholder is used to hold the cutting tool firmly during operation.
Dynamics of turning.
Forces.
The relative forces in a turning operation are important in the design of machine tools. The machine tool and its components must be able to withstand these forces without causing significant deflections, vibrations, or chatter during the operation. There are three principal forces during a turning process:
Speeds and feeds.
Speeds and feeds for turning are chosen based on cutter material, workpiece material, setup rigidity, machine tool rigidity and spindle power, coolant choice, and other factors.


Similarities between Wiener and LMS

The Least mean squares filter solution converges to the Wiener filter solution, assuming that the unknown system is LTI and the noise is stationary. Both filters can be used to identify the impulse response of an unknown system, knowing only the original input signal and the output of the unknown system. By relaxing the error criterion to reduce current sample error instead of minimizing the total error over all of n, the LMS algorithm can be derived from the Wiener filter.
Derivation of the Wiener filter for system identification.
Given a known input signal formula_1, the output of an unknown LTI system formula_2 can be expressed as:
formula_3
where formula_4 is an unknown filter tap coefficients and formula_5 is noise.
The model system formula_6, using a Wiener filter solution with an order N, can be expressed as:
formula_7
where formula_8 are the filter tap coefficients to be determined.
The error between the model and the unknown system can be expressed as:
formula_9
The total squared error formula_10 can be expressed as:
formula_11
formula_12
formula_13
Use the Minimum mean-square error criterion over all of formula_14 by setting its gradient to zero:
formula_15
which is
formula_16
for all formula_17
formula_18
Substitute the definition of formula_6:
formula_20
Distribute the partial derivative:
formula_21
Using the definition of discrete cross-correlation:
formula_22
formula_23
Rearrange the terms:
formula_24
for all formula_17
This system of N equations with N unknowns can be determined.
The resulting coefficients of the Wiener filter can be determined by: formula_26, where formula_27 is the cross-correlation vector between formula_28 and formula_29.
Derivation of the LMS algorithm.
By relaxing the infinite sum of the Wiener filter to just the error at time formula_14, the LMS algorithm can be derived.
The squared error can be expressed as:
formula_31
Using the Minimum mean-square error criterion, take the gradient:
formula_32
Apply chain rule and substitute definition of y[n]
formula_33
formula_34
Using gradient descent and a step size formula_35:
formula_36
which becomes, for i = 0, 1, ..., N-1,
formula_37
This is the LMS update equation.


Machining vibrations

In machining, vibrations, also called chatter, are the relative movements between the workpiece and the cutting tool. The vibrations result in waves on the machined surface. This affects typical machining processes, such as turning, milling and drilling, and atypical machining processes, such as grinding.
A chatter mark is an irregular surface flaw left by a wheel that is "out of true" (off-center) in grinding, or regular marks left when turning a long piece on a lathe, due to machining vibrations.
As early as 1907, Frederick W. Taylor described machining vibrations as the most obscure and delicate of all the problems facing the machinist, an observation still true today, as shown in many publications on machining.
The explanation of the machine tool regenerative chatter was made by Tobias. S. A. and W. Fishwick in 1958, by modeling the feedback loop between the metal cutting process and the machine tool structure, and came with the stability lobes diagram. The structure stiffness, damping ratio and the machining process damping factor, are the main parameters that defines the limit where the machining process vibration is prone to enlarge with time.
Mathematical models make it possible to simulate machining vibration quite accurately, but in practice it is always difficult to avoid vibrations.
Avoidance techniques.
Basic rules for the machinist for avoiding vibrations:
Industrial context.
The use of high speed machining (HSM) has enabled an increase in productivity and the realization of workpieces that were impossible before, such as thin walled parts. Unfortunately, machine centers are less rigid because of the very high dynamic movements. In many applications, i.e. long tools, thin workpieces, the appearance of vibrations is the most limiting factor and compels the machinist to reduce cutting speeds and feeds well below the capacities of machines or tools.
Vibration problems generally result in noise, bad surface quality and sometimes tool breakage. The main sources are of two types: forced vibrations and self-generated vibrations.
Forced vibrations are mainly generated by interrupted cutting (inherent to milling), runout, or vibrations from outside the machine.
Self generated vibrations are related to the fact that the actual chip thickness depends also on the relative position between tool and workpiece during the previous tooth passage. Thus increasing vibrations may appear up to levels which can seriously degrade the machined surface quality.
Laboratory research.
Industrial and academic researchers have widely studied machining vibration. Specific strategies have been developed, especially for thin-walled work pieces, by alternating small machining passes in order to avoid static and dynamic flexion of the walls. The length of the cutting edge in contact with the workpiece is also often reduced in order to limit self-generated vibrations.
The modeling of the cutting forces and vibrations, although not totally accurate, makes it possible to simulate problematic machining and reduce unwanted effects of vibration.
Multiplication of the models based on stability lobe theory, which makes it possible to find the best spindle speed for machining, gives robust models for any kind of machining.
Time domain simulations compute workpiece and tool position on very small time scales without great sacrifice in accuracy of the instability process and of the surface modeled. These models need more computing resources than stability lobe models, but give greater freedom (cutting laws, runout, ploughing, finite element models). Time domain simulations are quite difficult to robustify, but a lot of work is being done in this direction in the research laboratories.
In addition to stability lobe theory, the use of variable tool pitch often gives good results, at a relatively low cost. These tools are increasingly proposed by tool manufacturers, although this is not really compatible with a reduction in the number of tools used. Other research leads are also promising, but often need major modifications to be practical in machining centers.
Two kinds of software are very promising: Time domain simulations which give not yet reliable prediction but should progress, and vibration machining expert software, pragmatically based on knowledge and rules.
Industrial methods used to limit machining vibrations.
The usual method for setting up a machining process is still mainly based on historical technical knowhow and on trial and error method to determine the best parameters. According to the particular skills of a company, various parameters are studied in priority, such as depth of cut, tool path, workpiece set-up, and geometrical definition of the tool. When a vibration problem occurs, information is usually sought from the tool manufacturer or the CAM (Computer-aided manufacturing) software retailer, and they may give a better strategy for machining the workpiece. Sometimes, when vibration problems are too much of a financial prejudice, experts can be called upon to prescribe, after measurement and calculation, spindle speeds or tool modifications.
Compared to the industrial stakes, commercial solutions are rare. To analyse the problems and to propose solutions, only few experts propose their services. Computational software for stability lobes and measurement devices are proposed but, in spite of widespread publicity, they remain relatively rarely used. Lastly, vibration sensors are often integrated into machining centers but they are used mainly for wear diagnosis of the tools or the spindle.
New Generation Tool Holders and especially the Hydraulic Expansion Tool Holders minimise the undesirable effects of vibration to a large extent. First of all, the precise control of total indicator reading to less than 3 micrometres helps reduce vibrations due to balanced load on cutting edges and the little vibration created thereon is absorbed largely by the oil inside the chambers of the Hydraulic Expansion Tool Holder.
The machining vibration is often coming from the tool holder having a high L/D ratio and low stiffness. Stiffening the tool holder with tungsten carbide material is widely used when the tool diameter/weight is small, and the material cost of tungsten carbide is not high. A longer reach at L/D above 4 until 14, a mass damper is necessary to effectively damp out the vibration with a counteracting force to the tool structure. The simple form of mass damper has a heavy weight (made of tungsten or lead) supported by rubber rings, with or without a tuning mechanism. The tuning mechanism enables the mass damper to cover a wider L/D ratio (associated with vibration frequency) range. A more advanced mass damper on cutting tools use viscous fluid or damping oil to improve the dampening efficiency at the targeted L/D ratio (vibration frequency). The latest mass damper on cutting tools are making use of special polymers that has frequency dependent stiffness, and use these polymers to make both self-tuning/adjusting to cover a wider L/D ratio.
The machine tools with sensors integrated, which can measure the vibration in machining and provide a feedback to automatically tune the mass damper, is already demonstrated in lab-scale. The deployment of such solutions is still pending on its ease of use and cost.


OpenSTA

OpenSTA is a feature-rich GUI-based web server benchmarking utility that can perform scripted HTTP and HTTPS heavy load tests with performance measurements. It is freely available and distributable under the open source GNU General Public License. OpenSTA currently only runs on Microsoft Windows-based operating systems.
Scripts are recorded in a proprietary language called "SCL". It is a fairly simple coding language that provides support for custom functions, variable scopes, and random or sequential lists.
OpenSTA was originally written by Cyrano. Cyrano's intentions were to write commercial plug in modules and support for OpenSTA for performance testing non-web applications.
Note that the most recent version posted on the OpenSTA home page is 1.4.4, released 27 Oct, 2007.


Cray CX1000

The Cray CX1000 is a family of high-performance computers which is manufactured by Cray Inc., and consists of two individual groups of computer systems. The first group is intended for scale-up symmetric multiprocessing (SMP), and consists of the CX1000-SM and CX1000-SC nodes. The second group is meant for scale-out cluster computing, and consists of the CX1000 Blade Enclosure, and the CX1000-HN, CX1000-C and CX1000-G nodes.
The CX1000 line sits between Cray's entry-level CX-1 Personal Supercomputer range and Cray's high-end XT-series supercomputers.
CX1000 scale-up symmetric multiprocessing nodes.
The CX1000-SM and CX1000-SC nodes can be used for cluster computing, but they are designed for scale-up Symmetric Multi-Processing (SMP). When used for cluster computing, the CX1000-SM node is intended to be the "master (service)" node, although it can instead be a "compute" node. Similarly, the CX1000-SC node, when used for cluster computing, is intended to be a compute node, but can instead act as the master (service) node. Either or both the CX1000-SC and/or CX1000-SM nodes can be deployed in a HPC cluster. The CX1000-SM and CX1000-SC nodes, when used for SMP, are connected by a cache-coherency interconnect which is a built-in subassembly of the CX1000-SM and CX1000-SC nodes, rather than a standalone device, and is called the "Drawer Interconnect Switch" in Cray literature. The Drawer Interconnect Switch uses the Intel QuickPath Interconnect technology.
CX1000 scale-out cluster computing nodes.
The CX1000 scale-out cluster computing group of systems consists of the CX1000 Blade Enclosure, CX1000-C compute Node, CX1000-G GPU Node and CX1000-HN Management Node. Unlike the CX1000-SM and CX1000-SC nodes, these nodes cannot be used for scale-up SMP, as they were designed without a cache-coherency capability. The CX1000-C and CX1000-G nodes both have blade form factors, and the CX1000-HN node is a rackmount 2U Server. The CX1000-HN is intended to act as the head (service) node in an HPC cluster, with CX1000-C and/or CX1000-G compute nodes.


Neural Network Intelligence

NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. It is used to automate feature engineering, model compression, neural architecture search, and hyper-parameter tuning.
The source code is licensed under MIT License and available on GitHub.


IEC 61851

IEC 61851 is an international standard for electric vehicle conductive charging systems, parts of which are currently still under development(written 2017). IEC 61851 is one of the International Electrotechnical Commission's group of standards for electric road vehicles and electric industrial trucks and is the responsibility of IEC Technical Committee 69 (TC69).
Standard documents.
IEC 61851 consists of the following parts, detailed in separate IEC 61851 standard documents:
IEC 61851-1.
IEC 61851-1 defines four modes of charging:


HOSVD-based canonical form of TP functions and qLPV models

Based on the key idea of higher-order singular value decomposition (HOSVD) in tensor algebra, Baranyi and Yam proposed the concept of HOSVD-based canonical form of TP functions and quasi-LPV system models. Szeidl et al. proved that the TP model transformation is capable of numerically reconstructing this canonical form.
Related definitions (on TP functions, finite element TP functions, and TP models) can be found here. Details on the control theoretical background (i.e., the TP type polytopic Linear Parameter-Varying state-space model) can be found here.
A free MATLAB implementation of the TP model transformation can be downloaded at or at MATLAB Central .
Existence of the HOSVD-based canonical form.
Assume a given finite element TP function:
where formula_2. Assume that, the weighting functions in formula_3 are othonormal (or we transform to) for formula_4. Then, the execution of the HOSVD on the core tensor formula_5 leads to:
Then,
that is:
where weighting functions of formula_9 are orthonormed (as both the formula_3 and formula_11 where orthonormed) and core tensor formula_12 contains the higher-order singular values.
Definition.
&* ordering: formula_33 for all possible values of formula_34.


Transfer line

A transfer line is a manufacturing system which consists of a predetermined sequence of machines connected by an automated material handling system and designed for working on a very small family of parts. Parts can be moved singularly because there’s no need for batching when carrying parts between process stations (as opposed to a job shop for example). The line can synchronous, meaning that all parts advance with the same speed, or asynchronous, meaning buffers exist between stations where parts wait to be processed. Not all transfer lines must geometrically be straight lines, for example circular solutions have been developed which make use of rotary tables, however using buffers becomes almost impossible.
A crucial problem for this production system is that of line balancing: a trade-off between increasing productivity and minimizing cost conserving total processing time.


Vibrating feeder

A vibratory feeder is an instrument that uses vibration to "feed" material to a process or machine. Vibratory feeders use both vibration and gravity to move material. Gravity is used to determine the direction, either down, or down and to a side, and then vibration is used to move the material. They are mainly used to transport a large number of smaller objects.
A belt weigher are used only to measure the material flow rate but weigh feeder can measure the flow material and also control or regulate the flow rate by varying the belt conveyor speed.
Industries Served.
Versatile and rugged vibratory bowl feeders have been extremely used for automatic feeding of small to large and differently shaped industrial parts. They are the oldest but still commonly used automation machine available for aligning and feeding machine parts, electronic parts, plastic parts, chemicals, metallic parts, glass vials, pharmaceuticals, foods, miscellaneous goods etc.
Available in standard and custom designs, vibratory bowl feeders have been largely purchased by varied industrial sectors for automating high-speed production lines and assembly systems. Some of the industries that use the service of this automation machine include:
With these easy-to-use and high-performing part-feeding machines, customers from varied industrial sectors have achieved lower error rates, less power consumption, better profits, better rates of efficiency and less dependency on manpower.


List of Logitech Racing Wheels compatible games

This is a list of video games compatible with Logitech's GT Force, Driving Force, Driving Force Pro, Driving Force GT, G29, G923, G25, G27, Logitech Momo Racing, Logitech Speed Force Force Feedback Wheel for Gamecube and Logitech Wii Speed Force Wireless Wheel.
Compatible games.
Yes* : works without H-pattern shifter.
Note : G25 has a sequential gear shift option that makes it more compatible so this chart is inaccurate for the G27 which does not have that feature. Both G25 and G27 have H-pattern shifter, but only the G25 allows for sequential shift.
Note : G29 has new software and a lot of games do not support this wheel or is partly compatible.
Note : Only G29 works with PS4, other wheels needs additional adapters.
Note : MOMO Racing was advertised as a PC wheel and it isn't compatible with some games on PS2. eg. PS2 era "Gran Turismo" series and "Initial D: Special Stage" don't recognize the wheel when plugged in.
Note : None of the EA games on Steam work with the G29. It's most likely because the Origin launcher takes priority over the Steam launcher, so the Steam community steering wheel schemes aren't being read by the games. Test it yourself, you have 1 hour to try and get the wheel to work before you can request for a full refund.


Stochastic control

Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. Stochastic control aims to design the time path of the controlled variables that performs the desired control task with minimum cost, somehow defined, despite the presence of this noise. The context may be either discrete time or continuous time.
Certainty equivalence.
An extremely well-studied formulation in stochastic control is that of linear quadratic Gaussian control. Here the model is linear, the objective function is the expected value of a quadratic form, and the disturbances are purely additive. A basic result for discrete-time centralized systems with only additive uncertainty is the certainty equivalence property: that the optimal control solution in this case is the same as would be obtained in the absence of the additive disturbances. This property is applicable to all centralized systems with linear equations of evolution, quadratic cost function, and noise entering the model only additively; the quadratic assumption allows for the optimal control laws, which follow the certainty-equivalence property, to be linear functions of the observations of the controllers.
Any deviation from the above assumptions—a nonlinear state equation, a non-quadratic objective function, noise in the multiplicative parameters of the model, or decentralization of control—causes the certainty equivalence property not to hold. For example, its failure to hold for decentralized control was demonstrated in Witsenhausen's counterexample.
Discrete time.
In a discrete-time context, the decision-maker observes the state variable, possibly with observational noise, in each time period. The objective may be to optimize the sum of expected values of a nonlinear (possibly quadratic) objective function over all the time periods from the present to the final period of concern, or to optimize the value of the objective function as of the final period only. At each time period new observations are made, and the control variables are to be adjusted optimally. Finding the optimal solution for the present time may involve iterating a matrix Riccati equation backwards in time from the last period to the present period.
In the discrete-time case with uncertainty about the parameter values in the transition matrix (giving the effect of current values of the state variables on their own evolution) and/or the control response matrix of the state equation, but still with a linear state equation and quadratic objective function, a Riccati equation can still be obtained for iterating backward to each period's solution even though certainty equivalence does not apply.ch.13 The discrete-time case of a non-quadratic loss function but only additive disturbances can also be handled, albeit with more complications.
Example.
A typical specification of the discrete-time stochastic linear quadratic control problem is to minimize
where E1 is the expected value operator conditional on "y"0, superscript T indicates a matrix transpose, and "S" is the time horizon, subject to the state equation
where "y" is an "n" × 1 vector of observable state variables, "u" is a "k" × 1 vector of control variables, "A""t" is the time "t" realization of the stochastic "n" × "n" state transition matrix, "B""t" is the time "t" realization of the stochastic "n" × "k" matrix of control multipliers, and "Q" ("n" × "n") and "R" ("k" × "k") are known symmetric positive definite cost matrices. We assume that each element of "A" and "B" is jointly independently and identically distributed through time, so the expected value operations need not be time-conditional.
Induction backwards in time can be used to obtain the optimal control solution at each time,
with the symmetric positive definite cost-to-go matrix "X" evolving backwards in time from formula_4 according to
which is known as the discrete-time dynamic Riccati equation of this problem. The only information needed regarding the unknown parameters in the "A" and "B" matrices is the expected value and variance of each element of each matrix and the covariances among elements of the same matrix and among elements across matrices.
The optimal control solution is unaffected if zero-mean, i.i.d. additive shocks also appear in the state equation, so long as they are uncorrelated with the parameters in the "A" and "B" matrices. But if they are so correlated, then the optimal control solution for each period contains an additional additive constant vector. If an additive constant vector appears in the state equation, then again the optimal control solution for each period contains an additional additive constant vector.
The steady-state characterization of "X" (if it exists), relevant for the infinite-horizon problem in which "S" goes to infinity, can be found by iterating the dynamic equation for "X" repeatedly until it converges; then "X" is characterized by removing the time subscripts from its dynamic equation.
Continuous time.
If the model is in continuous time, the controller knows the state of the system at each instant of time. The objective is to maximize either an integral of, for example, a concave function of a state variable over a horizon from time zero (the present) to a terminal time "T", or a concave function of a state variable at some future date "T". As time evolves, new observations are continuously made and the control variables are continuously adjusted in optimal fashion.
Stochastic model predictive control.
In the literature, there are two types of MPCs for stochastic systems; Robust model predictive control and Stochastic Model Predictive Control (SMPC). Robust model predictive control is a more conservative method which considers the worst scenario in the optimization procedure. However, this method, similar to other robust controls, deteriorates the overall controller's performance and also is applicable only for systems with bounded uncertainties. The alternative method, SMPC, considers soft constraints which limit the risk of violation by a probabilistic inequality.
In finance.
In a continuous time approach in a finance context, the state variable in the stochastic differential equation is usually wealth or net worth, and the controls are the shares placed at each time in the various assets. Given the asset allocation chosen at any time, the determinants of the change in wealth are usually the stochastic returns to assets and the interest rate on the risk-free asset. The field of stochastic control has developed greatly since the 1970s, particularly in its applications to finance. Robert Merton used stochastic control to study optimal portfolios of safe and risky assets. His work and that of Black–Scholes changed the nature of the finance literature. Influential mathematical textbook treatments were by Fleming and Rishel, and by Fleming and Soner. These techniques were applied by Stein to the financial crisis of 2007–08.
The maximization, say of the expected logarithm of net worth at a terminal date "T", is subject to stochastic processes on the components of wealth. In this case, in continuous time Itô's equation is the main tool of analysis. In the case where the maximization is an integral of a concave function of utility over an horizon (0,"T"), dynamic programming is used. There is no certainty equivalence as in the older literature, because the coefficients of the control variables—that is, the returns received by the chosen shares of assets—are stochastic.


Java Agent Development Framework

Java Agent Development Framework, or JADE, is a software framework for the development of software agents, implemented in Java. JADE system supports coordination between several agents FIPA and provides a standard implementation of the communication language FIPA-ACL, which facilitates the communication between agents and allows the services detection of the system. JADE was originally developed by Telecom Italia and is distributed as free software.
Resume.
JADE is a middleware which facilitates the development of multi-agent systems under the standard FIPA for which purpose it creates multiple containers for agents, each of them can run on one or more systems. It's understood that a set of containers constitutes a platform.
JADE provides:
History.
JADE was initially developed by Telecom Italia Lab. This sector is the R & D branch of Telecom Italia Group which is responsible for promoting technological innovation. Telecom Italia conceived and promoted JADE by basing it in 2000. The latest available dates from June 2017 (version 4.5). The first version of JADE distributed as free software is available from February 2000 (versión 1.3).
In March 2003 Motorola and Telecom Italia created the JADE Governing Board with the objective of promoting the development and adoption of JADE in the mobile telecommunications industry as middleware based. The JADE Governing Board accepts to any company and/or organization interested in the commercial use and exploitation of JADE to commit to its development and promotion.
In 2021, the team that successfully developed JADE announced that they could not continue to work on it anymore. A team of researcher forked it and is now pursuing the platform development.
Platform.
JADE is a distributed agents platform, which has a container for each host where you are running the agents. Additionally, the platform has various debugging tools, mobility of code and content agents, the possibility of parallel execution of the behavior of agents, as well as support for the definition of languages and ontologies.
Each platform must have a parent container that has two special agents called AMS and DF.
DF Agent.
To access the DF agent the class "jade.domain.DFService" and its static methods are used: "register, deregister, modify" and "Search".
AMS agent.
To access the AMS Service an agent is created which automatically runs the "register" method of the AMS by default before executing the method "setup" from the new agent. When an agent is destroyed it executes its "takeDown" method by default and automatically calls the "deregister" method of the AMS.
Agent class.
The Agent class is a superclass which allows the users to create JADE agents. To create an agent one needs to inherit directly from "Agent". Normally, each agent recorder several services which they should be implemented by one or more behaviors.
This class provides methods to perform the basic tasks of the agents as:
JADE agent.
The cycle of life of a JADE agent follows the cycle proposed by FIPA. These agents go through different states defined as:
Agents' behaviour.
The behavior defines the actions under a given event. This behavior of the agent is defined in the method "setup" using the method "addBehaviour".
The different behaviors that the agent will adopt are defined from the abstract class Behaviour. The class Behaviour contains the abstract methods:
A user can override the methods "onStart " and" OnEnd " property. Additionally, there are other methods such as block" " and" restart " used for modifying the agent's behavior. When an agent is locked it can be unlocked in different ways.
Otherwise the user can override the methods "onStart" and "onEnd" the agent possess.
ACL messages.
Message passing ACL (Agent Communication Language) is the base of communication between agents. Sending messages is done by the method "send" of the class Agent. In this method, you have to pass an object of type ACLMessage that contains the recipient information, language, coding and content of the message.
These messages are sent asynchronously, while messages are received they will be stored in a message queue. There are two types of receiving ACL messages, blocking or non-blocking. For this provide methods "blockingReceive " and "receive " respectively. In both methods, you can make filtering messages to be retrieved from the queue by setting different templates.
Extensions.
JADE has an extension denominated WADE (Workflows and Agents Development Environment) which is a system of workflow which allows create process by a graphic editor named WOLF.


MYTV International

MYTV International Ltd is a company in digital entertainment media industry. The field of operations includes business music and music video service for DJs and VJs, IPTV development, and digital signage. MYTV product family combines the multimedia and networking technology to produce a digital media networking system that provides control and flexibility of delivering entertaining and promotional content. The products of MYTV International Ltd. are TrendSign and miXJay.
MYTV was established in Finland in 2000. At the beginning MYTV was part of UCM Group Oy. CEO Petteri Honkaniemi was founder of MYTV concept in UCM Group.
In January 2002 shareholders of MYTV International bought the business model from publishing company UCM Group and started to develop the MYTV concept as core business of MYTV International and MYTV Finland Ltd. MYTV International is private owned company. At the present MYTV International has offices in 9 countries worldwide.