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Control engineering

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Control systems play a critical role in space flight.

Control engineering or control systems engineering or Automation engineering (In Some European Countries) is an engineering discipline that deals with control systems, applying control theory to design equipment and systems with desired behaviors in control environments.[1] The discipline of controls overlaps and is usually taught along with electrical engineering, chemical engineering and mechanical engineering at many institutions around the world.[1]

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.[2]

Overview

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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

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See also: Control systems § History
Control of fractionating columns is one of the more challenging applications.

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 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.

Mathematical modelling

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David Quinn Mayne, (1930–2024) was among the early developers of a rigorous mathematical method for analysing Model predictive control algorithms (MPC). It is currently used in tens of thousands of applications and is a core part of the advanced control technology by hundreds of process control producers. MPC's major strength is its capacity to deal with nonlinearities and hard constraints in a simple and intuitive fashion. His work underpins a class of algorithms that are provably correct, heuristically explainable, and yield control system designs which meet practically important objectives.[3]

Control systems

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This section is an excerpt from Control system.[edit]
The centrifugal governor is an early proportional control mechanism.

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process.

For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable (PV) being controlled with the desired value or setpoint (SP), and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint.

For sequential and combinational logic, software logic, such as in a programmable logic controller, is used.[clarification needed]

Control theory

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This section is an excerpt from Control theory.[edit]

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.[4] 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.[5]

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.[6]

Education

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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,[7] 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 [8] or the Department of Robotics and Control Engineering at the United States Naval Academy[9] and the Department of Control and Automation Engineering at the Istanbul Technical University.[10]

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

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A control engineer's career starts with a bachelor's degree and can continue through the college process. Control engineer degrees are typically paired with an electrical or mechanical engineering degree, but can also be paired with a degree in chemical engineering. According to a Control Engineering survey, most of the people who answered were control engineers in various forms of their own career.[11]

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.[11]

Because of this, there are many job opportunities in aerospace companies, manufacturing companies, automobile companies, power companies, chemical companies, petroleum companies, and government agencies. Some places that hire Control Engineers include companies such as Rockwell Automation, NASA, Ford, Phillips 66, Eastman, and Goodrich.[12] Control Engineers can possibly earn $66k annually from Lockheed Martin Corp. They can also earn up to $96k annually from General Motors Corporation.[13] Process Control Engineers, typically found in Refineries and Specialty Chemical plants, can earn upwards of $90k annually.

Recent advancement

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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.[14][15]

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.[16]

See also

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References

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  1. ^ a b "Systems & Control Engineering FAQ | Electrical Engineering and Computer Science". engineering.case.edu. Case Western Reserve University. 20 November 2015. Retrieved 27 June 2017.
  2. ^ Burns, S. Roland. Advanced Control Engineering. Butterworth-Heinemann. Auckland, 2001. ISBN 0750651008
  3. ^ Parisini, Thomas; Astolfi, Alessandro (10 June 2024). "Professor David Q Mayne FREng FRS 1930 - 2024". Imperial College London news. Retrieved 14 June 2024.
  4. ^ Maxwell, J. C. (1868). "On Governors" (PDF). Proceedings of the Royal Society. 100. Archived (PDF) from the original on 2008-12-19.
  5. ^ Minorsky, Nicolas (1922). "Directional stability of automatically steered bodies". Journal of the American Society of Naval Engineers. 34 (2): 280–309. doi:10.1111/j.1559-3584.1922.tb04958.x.
  6. ^ GND. "Katalog der Deutschen Nationalbibliothek (Authority control)". portal.dnb.de. Retrieved 2020-04-26.
  7. ^ Zhang, Jianhua (2017). Mechatronics and Automation Engineering. doi:10.1142/10406. ISBN 978-981-320-852-0.
  8. ^ "ACSE - The University of Sheffield". Retrieved 17 March 2015.
  9. ^ "WRC Home". USNA Weapons, Robotics and Control Engineering. Retrieved 19 November 2019.
  10. ^ "İTÜ Control and Automation Engineering". Kontrol ve Otomasyon Mühendisliği. Retrieved 2022-12-05.
  11. ^ a b "Career & Salary Report" (PDF). Control Engineering. 1 May 2019. Retrieved 5 December 2022.
  12. ^ "Systems & Control Engineering FAQ | Computer and Data Science/Electrical, Computer and Systems Engineering". engineering.case.edu. 2015-11-20. Retrieved 2019-10-30.
  13. ^ "Control Systems Engineer Salary | PayScale". www.payscale.com. Retrieved 2019-10-30.
  14. ^ Tan, K.C.; Li, Y. (2001). "Performance-based control system design automation via evolutionary computing" (PDF). Engineering Applications of Artificial Intelligence. 14 (4): 473–486. doi:10.1016/S0952-1976(01)00023-9. Archived (PDF) from the original on 2015-05-03.
  15. ^ Li, Yun; Ang, Kiam Heong; Chong, Gregory C. Y.; Feng, Wenyuan; Tan, Kay Chen; Kashiwagi, Hiroshi (2004). "CAutoCSD-evolutionary search and optimisation enabled computer automated control system design" (PDF). International Journal of Automation and Computing. 1: 76–88. doi:10.1007/s11633-004-0076-8. S2CID 55417415. Archived (PDF) from the original on 2012-01-27.
  16. ^ Rieger, Craig G.; Gertman, David I.; McQueen, Miles. A. (2009). "Resilient control systems: Next generation design research". 2009 2nd Conference on Human System Interactions. pp. 632–636. doi:10.1109/HSI.2009.5091051. ISBN 978-1-4244-3959-1. S2CID 6603922.

Further reading

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  • D. Q. Mayne (1965). P. H. Hammond (ed.). A Gradient Method for Determining Optimal Control of Nonlinear Stochastic Systems in Proceedings of IFAC Symposium, Theory of Self-Adaptive Control Systems. Plenum Press. pp. 19–27.
  • Bennett, Stuart (June 1986). A history of control engineering, 1800-1930. IET. ISBN 978-0-86341-047-5.
  • Bennett, Stuart (1993). A history of control engineering, 1930-1955. IET. ISBN 978-0-86341-299-8.
  • Christopher Kilian (2005). Modern Control Technology. Thompson Delmar Learning. ISBN 978-1-4018-5806-3.
  • Arnold Zankl (2006). Milestones in Automation: From the Transistor to the Digital Factory. Wiley-VCH. ISBN 978-3-89578-259-6.
  • Franklin, Gene F.; Powell, J. David; Emami-Naeini, Abbas (2014). Feedback control of dynamic systems (7th ed.). Stanford Cali. U.S.: Pearson. p. 880. ISBN 9780133496598.
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