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Multiple‐model adaptive control using set‐valued observers
Author(s) -
Rosa Paulo,
Silvestre Carlos
Publication year - 2013
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3005
Subject(s) - control theory (sociology) , set (abstract data type) , controller (irrigation) , computer science , identification (biology) , adaptive control , stability (learning theory) , state (computer science) , system identification , control (management) , mathematical optimization , mathematics , algorithm , artificial intelligence , data modeling , machine learning , botany , database , agronomy , biology , programming language
SUMMARY A multiple‐model adaptive control methodology is proposed that is able to provide stability and performance guarantees, for uncertain linear parameter‐varying plants. The identification problem is addressed by taking advantage of recent advances in model falsification using set‐valued observers (SVOs). These SVOs provide set‐valued estimates of the state of the system, according to its dynamic model. If such estimate is the empty set, the underlying dynamic model is invalidated, and a different controller is connected to the loop. The behavior of the proposed control algorithm is demonstrated in simulation, by resorting to a mass–spring–dashpot system. As a caveat, the computational burden associated with the SVOs can be prohibitive under some circumstances. Copyright © 2013 John Wiley & Sons, Ltd.