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Adaptive neural output feedback control for uncertain nonlinear systems with input quantization and output constraints
Author(s) -
Wang Min,
Zhang Tianping,
Yang Yuequan
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.3079
Subject(s) - control theory (sociology) , quantization (signal processing) , nonlinear system , lyapunov function , adaptive control , computer science , controller (irrigation) , nonlinear control , artificial neural network , control (management) , algorithm , artificial intelligence , physics , quantum mechanics , agronomy , biology
Summary In this paper, we are concerned with the problem of adaptive output‐feedback tracking control for nonlinear systems with input quantization, unmodeled dynamics, and output constraints. A novel quantizer with the advantages of hysteresis and uniform quantizer is introduced to handle input signals. A barrier Lyapunov function is employed to solve the output constraints. The state unmodeled dynamics is solved by using a Lyapunov description, and neural networks are used to approximate the unknown smooth functions produced in the adaptive control design process. The controller design is simplified by combining the new quantizer with dynamic surface control method. The mathematical derivation shows the stability of the closed‐loop system and the effectiveness of output constraints. Simulation illustrates and clarifies the theoretical findings.