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Neural adaptive prescribed performance control for interconnected nonlinear systems with output dead zone
Author(s) -
Du Peihao,
Zhou Qi,
Liang Hongjing
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.4802
Subject(s) - backstepping , control theory (sociology) , nonlinear system , dead zone , bounded function , controller (irrigation) , computer science , artificial neural network , adaptive control , lyapunov function , scheme (mathematics) , tracking error , set (abstract data type) , control (management) , lyapunov stability , control engineering , mathematics , engineering , artificial intelligence , mathematical analysis , oceanography , physics , quantum mechanics , agronomy , biology , programming language , geology
Summary This paper is concerned with the neural‐based decentralized adaptive control for interconnected nonlinear systems with prescribed performance and unknown dead zone outputs. In the controller design procedure, neural networks are employed to identify unknown auxiliary functions, and the control design obstacle caused by the output nonlinearity is resolved via introducing Nussbaum function. Then, a reliable neural decentralized adaptive control is developed through incorporating the backstepping method and the prescribed performance technique. In the light of Lyapunov stability theory, it is verified that the proposed control scheme can ensure that all the closed‐loop signals are bounded, and can also guarantee that the tracking errors remain within a small enough compact set with the prescribed performance bounds. Finally, some simulation results are given to illustrate the feasibility of the devised control strategy.