Premium
Observer‐based decentralized adaptive neural control for uncertain interconnected systems with input quantization and time‐varying output constraints
Author(s) -
Zhang Tianping,
Wang Min,
Xia Meizhen,
Yang Yuequan
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.5028
Subject(s) - control theory (sociology) , nonlinear system , quantization (signal processing) , bounded function , observer (physics) , lyapunov function , backstepping , computer science , state observer , controller (irrigation) , artificial neural network , mathematics , adaptive control , control (management) , algorithm , artificial intelligence , physics , mathematical analysis , agronomy , biology , quantum mechanics
Summary In this article, observer‐based decentralized adaptive neural control is proposed for uncertain interconnected nonlinear systems with input quantization and time‐varying output restrictions. Decentralized hysteresis quantizer is employed to handle input signal. The unmeasured states are estimated by designing K‐filters. A Lyapunov description is used to dispose of state unmodeled dynamics. The constrained interconnected nonlinear systems are transformed into novel interconnected nonlinear systems without output constraints by constructing invertible nonlinear mappings. Dynamic surface control technique and a hyperbolic tangent function are adopted to design decentralized controller, which has a simpler structure. Stability analysis indicates that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded and system outputs satisfy the constraints. Two numerical examples are used to verify the effectiveness of the theoretical findings.