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An adaptive dynamic surface control of output constrained stochastic nonlinear systems with unknown control directions
Author(s) -
Shen Fei,
Wang Xinjun,
Yin Xinghui,
Jin Lingling
Publication year - 2020
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.3118
Subject(s) - backstepping , control theory (sociology) , nonlinear system , controller (irrigation) , bounded function , constraint (computer aided design) , adaptive control , tracking error , computer science , mathematics , control (management) , artificial intelligence , physics , mathematical analysis , geometry , quantum mechanics , agronomy , biology
Summary This article is concerned about an adaptive dynamic surface control (DSC) of output constrained stochastic nonlinear systems with unknown control directions and unmodeled dynamics. Nonlinear mapping‐based backstepping control design is presented for stochastic nonlinear systems with output constraint. The explosion of complexity exists in tradition backstepping method is avoided by using the DSC technique. The radial basis function neural networks are employed to deal with unknown nonlinear functions. Nussbaum gain technique is employed to handle the unknown control directions. And a dynamic signal is employed to dominate the unmodeled dynamics. The adaptive controller is designed can ensure that the tracking error converges on a small region of the origin. And all signals of the closed‐loop systems are semiglobal uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectivity of the designed controller scheme.