Content deleted Content added
Starlighsky (talk | contribs) mNo edit summary |
|||
| (12 intermediate revisions by 4 users not shown) | |||
Line 1:
{{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]].]]
Line 10 ⟶ 11:
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.<ref name="Keviczky_2019">{{Cite book |last=Keviczky |first=László |title=Control engineering |last2=Bars |first2=Ruth |last3=Hetthéssy |first3=Jenő |last4=Bányász |first4=Csilla |date=2019 |publisher=Springer |isbn=978-981-13-4114-4 |series=Advanced textbooks in control and signal processing |___location=Singapore}}</ref>{{rp|6}} 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 [[
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.
Line 22 ⟶ 23:
[[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.
{{r|Keviczky_2019|p=22}} 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{{r|Keviczky_2019|p=22}} 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.
Line 31 ⟶ 34:
===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 ==
Line 45 ⟶ 48:
== Careers ==
{{Globalize
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.{{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 ==
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:
* 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>
| |||