Gain scheduling

{{short description|Approach to control of non-linear systems}}

In control theory, gain scheduling is an approach to control of nonlinear systems that uses a family of linear controllers, each of which provides satisfactory control for a different operating point of the system.

One or more observable variables, called the scheduling variables, are used to determine what operating region the system is currently in and to enable the appropriate linear controller. For example, in an aircraft flight control system, the altitude and Mach number might be the scheduling variables, with different linear controller parameters available (and automatically plugged into the controller) for various combinations of these two variables. In brief, gain scheduling is a control design approach that constructs a nonlinear controller for a nonlinear plant by patching together a collection of linear controllers.

A relatively large scope state of the art about gain scheduling has been published in (Survey of Gain-Scheduling Analysis & Design, D.J.Leith, WE.Leithead).{{cite web |url=http://eprints.nuim.ie/1834/1/1001965764_link_19992.pdf|accessdate=1 November 2012|title=Survey of Gain-Scheduling Analysis & Design}}

Recently, new methodologies using Machine learning, such as Adaptive control based on Artificial Neural Networks (ANN) and Reinforcement Learning,{{Cite journal |last1=Hosseini |first1=Ehsan |last2=Aghadavoodi |first2=Ehsan |last3=Fernández Ramírez |first3=Luis M. |date=September 2020 |title=Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning |url=https://linkinghub.elsevier.com/retrieve/pii/S0960148120307588 |journal=Renewable Energy |language=en |volume=157 |pages=897–910 |doi=10.1016/j.renene.2020.05.060|bibcode=2020REne..157..897H |url-access=subscription }}{{Cite journal |last1=Yeh |first1=Yi-Liang |last2=Yang |first2=Po-Kai |date=2021-11-26 |title=Design and Comparison of Reinforcement-Learning-Based Time-Varying PID Controllers with Gain-Scheduled Actions |journal=Machines |language=en |volume=9 |issue=12 |pages=319 |doi=10.3390/machines9120319 |doi-access=free |issn=2075-1702}}{{Cite journal |last1=Gutiérrez-Oribio |first1=Diego |last2=Stathas |first2=Alexandros |last3=Stefanou |first3=Ioannis |date=2024-12-17 |title=AI-Driven Approach for Sustainable Extraction of Earth's Subsurface Renewable Energy While Minimizing Seismic Activity |url=https://onlinelibrary.wiley.com/doi/10.1002/nag.3923 |journal=International Journal for Numerical and Analytical Methods in Geomechanics |language=en |doi=10.1002/nag.3923 |issn=0363-9061|arxiv=2408.03664 }} have been studied.