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{{Short description|Engineering discipline that deals with control systems}}
{{confuse|Automation engineering}}
{{More citations needed|date=October 2022}}
[[File:Space Shuttle Columbia launching.jpg|thumb|320px|Control systems play a critical role in [[space flight]].]]
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[[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 [[
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.
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===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
== Control systems ==
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== Careers ==
{{Globalize|section|US|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>
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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.{{Cn|date=January 2025}}
In India, control System Engineering is provided at different levels with a diploma, graduation and postgraduation. These programs require the candidate to have chosen physics, chemistry and mathematics for their secondary schooling or relevant bachelor's degree for postgraduate studies.<ref>{{Cite web |title=Control System Engineering: Admission 2025, Fees, Syllabus, Entrance Exam, Top Colleges, Career Scope |url=https://www.shiksha.com/engineering/control-systems-chp |archive-url=http://web.archive.org/web/20250126115019/https://www.shiksha.com/engineering/control-systems-chp |archive-date=2025-01-26 |access-date=2025-01-31 |website=www.shiksha.com |language=en}}</ref>
== 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]].{{r|Keviczky_2019|p=23}} 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:
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.▼
* 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.
▲
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>
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