Editing Control engineering (section)

== 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. 

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>