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{{Short description|Engineering discipline that deals with control systems}}
{{More citations needed|date=October 2022}}
[[File:Space Shuttle Columbia launching.jpg|thumb|320px|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 system]]s, applying [[control theory]] to design equipment and systems with desired behaviors in control environments.<ref name="Case Western Reserve University">{{cite web|title=Systems & Control Engineering FAQ {{!}} Electrical Engineering and Computer Science|url=https://engineering.case.edu/eecs/node/213|website=engineering.case.edu|publisher=Case Western Reserve University|access-date=27 June 2017|language=en|date=20 November 2015}}</ref> The discipline of controls overlaps and is usually taught along with [[electrical engineering]], [[chemical engineering]] and [[mechanical engineering]] at many institutions around the world.<ref name="Case Western Reserve University"/>

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 model]]ing of a diverse range of [[system]]s.<ref>Burns, S. Roland. Advanced Control Engineering. Butterworth-Heinemann. Auckland, 2001. ISBN 0750651008</ref>

==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 [[Controller (control theory)|controller]]s for those systems; and to implement controllers in physical systems employing available technology. A [[control system|system]] can be [[mechanical engineering|mechanical]], [[electrical engineering|electrical]], [[fluid]], [[chemical]], [[financial control|financial]] or [[biology|biological]], and its mathematical modelling, analysis and controller design uses [[control theory]] in one or many of the [[Time domain|time]], [[frequency domain|frequency]] and [[S domain|complex-s]] domains, depending on the nature of the design problem.

Control engineering is the engineering [[discipline]] that focuses on the [[mathematical model|modeling]] of a diverse range of [[dynamic systems]] (e.g. [[mechanics|mechanical]] [[system]]s) and the design of [[controller (control theory)|controller]]s 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 circuit]]s, [[digital signal processor]]s and [[microcontroller]]s can all be used to implement [[control system]]s. Control engineering has a wide range of applications from the flight and propulsion systems of [[Airliner|commercial airliners]] to the [[cruise control]] present in many modern [[automobile]]s.

In most cases, control engineers utilize [[feedback]] when designing [[control system]]s. 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 [[Internal combustion engine|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 theory|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 [[sensor]]s.

== History ==
{{see also|Control systems#History}}
[[File:Colonne distillazione.jpg|thumb| Control of [[fractionating column]]s 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 [[Siege of Baghdad (1258)|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 [[Cornelis Drebbel|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 [[Centrifugal governor|governor]]. Later on, previous to modern [[power electronics]], process control systems for industrial applications were devised by mechanical engineers using [[pneumatics|pneumatic]] and [[Hydraulic system|hydraulic]] control devices, many of which are still in use today.

===Mathematical modelling===
[[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.<ref name=P&A>{{cite web|author1=Parisini, Thomas|author2=Astolfi, Alessandro|title=Professor David Q Mayne FREng FRS 1930 - 2024|url=https://www.imperial.ac.uk/news/253973/professor-david-mayne-freng-frs-1930/|date= 10 June 2024|publisher=[[Imperial College London]] news|access-date=14 June 2024}}</ref>

== Control systems ==
{{excerpt|Control system}}

== Control theory ==
{{excerpt|Control theory}}

== 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]],<ref>{{cite book|doi=10.1142/10406|title=Mechatronics and Automation Engineering |year=2017 |last1=Zhang |first1=Jianhua |isbn=978-981-320-852-0 }}</ref> 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 <ref>{{cite web|url=https://www.sheffield.ac.uk/acse|title=ACSE - The University of Sheffield|access-date=17 March 2015}}</ref> or the Department of Robotics and Control Engineering at the United States Naval Academy<ref>{{cite web |title=WRC Home |url=http://www.usna.edu/WSE/ |access-date=19 November 2019 |website=USNA Weapons, Robotics and Control Engineering}}</ref> and the Department of Control and Automation Engineering at the Istanbul Technical University.<ref>{{Cite web |title=İTÜ Control and Automation Engineering |url=https://kontrol.itu.edu.tr/en |access-date=2022-12-05 |website=Kontrol ve Otomasyon Mühendisliği |language=en}}</ref>

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 transform]]ation and algebra respectively, and could be said to complete a basic control education.

== Careers ==
{{Globalize |section |United States |date=April 2023 |discuss=Talk:Control engineering#"Careers" section}}
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.<ref name="Control">{{Cite web |date=1 May 2019 |title=Career & Salary Report |url=https://www.controleng.com/wp-content/uploads/sites/2/2019/05/Control-Engineering-2019-Career-and-Salary-Study.pdf |access-date=5 December 2022 |website=Control Engineering}}</ref>

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.<ref name="Control"/>

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 Chemical|Eastman]], and Goodrich.<ref>{{Cite web|url=http://engineering.case.edu/eecs/node/213|title=Systems & Control Engineering FAQ {{!}} Computer and Data Science/Electrical, Computer and Systems Engineering|date=2015-11-20|website=engineering.case.edu|language=en|access-date=2019-10-30}}</ref> Control Engineers can possibly earn $66k annually from Lockheed Martin Corp. They can also earn up to $96k annually from General Motors Corporation.<ref>{{Cite web|url=https://www.payscale.com/research/US/Job=Control_Systems_Engineer/Salary|title=Control Systems Engineer Salary {{!}} PayScale|website=www.payscale.com|access-date=2019-10-30}}</ref> Process Control Engineers, typically found in [[Oil Refinery|Refineries]] and Specialty Chemical plants, can earn upwards of $90k annually.

== 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.<ref>{{cite journal|doi=10.1016/S0952-1976(01)00023-9|title=Performance-based control system design automation via evolutionary computing |year=2001 |last1=Tan |first1=K.C. |last2=Li |first2=Y. |journal=Engineering Applications of Artificial Intelligence |volume=14 |issue=4 |pages=473–486 |url=http://eprints.gla.ac.uk/3807/1/Dr3_Y_Li_paper1.pdf |archive-url=https://web.archive.org/web/20150503181152/http://eprints.gla.ac.uk/3807/1/Dr3_Y_Li_paper1.pdf |archive-date=2015-05-03 |url-status=live }}</ref><ref>{{cite journal|doi=10.1007/s11633-004-0076-8|title=CAutoCSD-evolutionary search and optimisation enabled computer automated control system design |year=2004 |last1=Li |first1=Yun |last2=Ang |first2=Kiam Heong |last3=Chong |first3=Gregory C. Y. |last4=Feng |first4=Wenyuan |last5=Tan |first5=Kay Chen |last6=Kashiwagi |first6=Hiroshi |journal=International Journal of Automation and Computing |volume=1 |pages=76–88 |s2cid=55417415 |url=http://eprints.gla.ac.uk/3818/1/IJAC_04_CAutoCSD.pdf |archive-url=https://web.archive.org/web/20120127151632/http://eprints.gla.ac.uk/3818/1/IJAC_04_CAutoCSD.pdf |archive-date=2012-01-27 |url-status=live }}</ref>

[[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.<ref>{{cite book|doi=10.1109/HSI.2009.5091051|chapter=Resilient control systems: Next generation design research |title=2009 2nd Conference on Human System Interactions |year=2009 |last1=Rieger |first1=Craig G. |last2=Gertman |first2=David I. |last3=McQueen |first3=Miles. A. |pages=632–636 |isbn=978-1-4244-3959-1 |s2cid=6603922 |url=https://digital.library.unt.edu/ark:/67531/metadc935383/ }}</ref>

== See also ==
{{cols|colwidth=18em}}
* [[Artificial intelligence]]
* [[Automation]]
* [[Automation engineering]]
* [[Electrical engineering]]
* [[Communications engineering]]
* [[Satellite navigation]]
* [[Outline of control engineering]]
* [[Advanced process control]]
* [[Building automation]]
* [[Computer-automated design]] (CAutoD, CAutoCSD)
* [[Control reconfiguration]]
* [[Feedback]]
* [[H-infinity]]
* [[Lead–lag compensator]]
* [[List of electrical engineering topics#Control engineering|List of control engineering topics]]
* [[Quantitative feedback theory]]
* [[Unicycle cart|Robotic unicycle]]
* [[State space (controls)|State space]]
* [[Sliding mode control]]
* [[Systems engineering]]
* [[TPT (Software)|Testing controller]]
* [[VisSim]]
* [[Control Engineering (magazine)]]
* [[Time series]]
* [[Process control system]]
* [[Robotic control]]
* [[Mechatronics]]
* [[SCADA]]
{{colend}}

==References==
{{Reflist}}

== Further reading ==
*{{Cite book | publisher = Plenum Press| editor = P. H. Hammond | author = [[David Quinn Mayne|D. Q. Mayne]]| title = A Gradient Method for Determining Optimal Control of Nonlinear Stochastic Systems in Proceedings of [[International Federation of Automatic Control|IFAC]] Symposium, Theory of Self-Adaptive Control Systems | pages = 19–27| date= 1965}}
* {{Cite book | publisher = IET | isbn = 978-0-86341-047-5 | last = Bennett | first = Stuart | title = A history of control engineering, 1800-1930 | date = June 1986}}
* {{Cite book | publisher = IET | isbn = 978-0-86341-299-8 | last = Bennett | first = Stuart | title = A history of control engineering, 1930-1955 | year = 1993 }}
*{{cite book | author= Christopher Kilian | title= Modern Control Technology | publisher= Thompson Delmar Learning | year= 2005 | isbn=978-1-4018-5806-3 }}
*{{cite book | author= Arnold Zankl | title= Milestones in Automation: From the Transistor to the Digital Factory | publisher= Wiley-VCH | year= 2006 | isbn= 978-3-89578-259-6}}
*{{cite book|last1=Franklin|first1=Gene F.|last2=Powell|first2=J. David|last3=Emami-Naeini|first3=Abbas|author-link1=Gene F. Franklin|title=Feedback control of dynamic systems|date=2014|publisher=Pearson|location=Stanford Cali. U.S.|isbn=9780133496598|pages=880|edition=7th|language=en}}

==External links==
{{wikibooks|Control Systems}}
{{Commons category}}
*[https://web.archive.org/web/20121211031854/http://www-control.eng.cam.ac.uk/extras/Virtual_Library/Control_VL.html Control Labs Worldwide]
*[https://web.archive.org/web/20080827235300/http://controls.engin.umich.edu/wiki The Michigan Chemical Engineering Process Dynamics and Controls Open Textbook]
*[http://www.controlsys.org Control System Integrators Association]
*[https://www.csiaexchange.com List of control systems integrators]
*[http://www.imeche.org/get-involved/special-interest-groups/mechatronics-informatics-and-control-group Institution of Mechanical Engineers - Mechatronics, Informatics and Control Group (MICG)] 
*[https://www.tandfonline.com/tssc Systems Science & Control Engineering: An Open Access Journal]

{{Engineering fields}}
{{Systems Engineering}}
{{Authority control}}

[[Category:Control engineering| ]]
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[[Category:Systems engineering]]
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