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Observer‐based adaptive control for nonlinear strict‐feedback stochastic systems with output constraints
Author(s) -
Xu Yuanyuan,
Liang Hongjing,
Li Tieshan,
Ma Hui
Publication year - 2018
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.4445
Subject(s) - control theory (sociology) , backstepping , nonlinear system , observer (physics) , lyapunov function , bounded function , adaptive control , state observer , constraint (computer aided design) , computer science , mathematics , control (management) , artificial intelligence , mathematical analysis , physics , geometry , quantum mechanics
Summary In this paper, an adaptive output‐feedback control problem is investigated for nonlinear strict‐feedback stochastic systems with input saturation and output constraint. A barrier Lyapunov function is used to solve the problem of output constraint. Then, fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. To overcome the difficulties in designing the control signal in the saturation, we introduce an auxiliary signal in the n + 1 t h step in the deduction. By combining Nussbaum technique and the adaptive backstepping technique, an adaptive output‐feedback control method is developed. The proposed control method not only overcomes the problem of the compensation for the nonlinear term from the input saturation but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed‐loop system are semiglobally uniformly ultimately bounded. Finally, the effectiveness of the proposed method is verified by the simulation results.