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Adaptive optimal dynamic surface control of strict‐feedback nonlinear systems with output constraints
Author(s) -
Zhang Tianping,
Xu Haoxiang
Publication year - 2019
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.4864
Subject(s) - control theory (sociology) , feed forward , controller (irrigation) , nonlinear system , dynamic programming , computer science , optimal control , bounded function , adaptive control , mathematical optimization , scheme (mathematics) , artificial neural network , control (management) , mathematics , control engineering , engineering , agronomy , mathematical analysis , physics , quantum mechanics , artificial intelligence , biology , machine learning
Summary In this paper, an adaptive optimal control strategy is proposed for a class of strict‐feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural‐network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme.