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Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints
Author(s) -
Hua Changchun,
Meng Rui,
Li Kuo
Publication year - 2020
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.5060
Subject(s) - control theory (sociology) , nonlinear system , computer science , controller (irrigation) , constraint (computer aided design) , lyapunov function , transformation (genetics) , stability (learning theory) , state (computer science) , adaptive control , mathematical optimization , control (management) , mathematics , algorithm , artificial intelligence , biochemistry , chemistry , physics , geometry , quantum mechanics , machine learning , gene , agronomy , biology
Summary This article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.