Multiple models
In control theory, multiple models is an approach to estimate the plant parameters. It usees large number of models, which are distributed in the region of uncertainty, and based on the responses of the plant and the models. One model is chosen at every instant, which is closest to the plant according to some metric.[1] ]
of non-linear 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.
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).[2]
See also
References
- ^ Narendra, Kumpati S.; Han, Zhuo (August 2011). "Adaptive Control Using Collective Information Obtained from Multiple Models". International Federation of Automatic Control. 18 (1): 362–367. doi:10.3182/20110828-6-IT-1002.02237.
- ^ "Survey of Gain-Scheduling Analysis & Design" (PDF). Retrieved 1 November 2012.
Further reading
- Briat, Corentin (2015). Linear Parameter-Varying and Time-Delay Systems - Analysis, Observation, Filtering & Control. Springer Verlag Heidelberg. ISBN 978-3-662-44049-0.