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A new robust model reference control with improved performance for a class of multivariable unknown plants
Author(s) -
Chien ChiangJu,
Fu LiChen
Publication year - 1992
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480060202
Subject(s) - multivariable calculus , control theory (sociology) , reference model , context (archaeology) , adaptive control , controller (irrigation) , lyapunov function , robust control , residual , stability (learning theory) , set (abstract data type) , computer science , tracking error , mathematics , control system , control engineering , control (management) , engineering , algorithm , artificial intelligence , nonlinear system , machine learning , paleontology , agronomy , physics , software engineering , electrical engineering , quantum mechanics , biology , programming language
Motivated by recent works on parametrization of multivariable plants for model reference adaptive control (MRAC), a new robust model reference control (MRC) scheme for a class of multivariable unknown plants is presented. The salient feature of this control scheme is the improved performance of the output‐tracking property, which is hardly attainable by the traditional MRAC schemes. The controller here is devised using the concept of variable structure design which prevails in the robust control context. It is shown by a Lyapunov approach that without any persistent excitation the global stability of the overall system is achieved and the tracking errors will converge to a residual set. The size of that set can be directly related to the size of unmodelled dynamics and output disturbances explicitly as long as a set of control parameters is chosen properly (large).