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Adaptive robust control for networked strict-feedback nonlinear systems with state and input quantization
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Resumo(s)
Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.
Descrição
Grant 62022031 Grant 61773135 Grant U20A20188
Palavras-chave
Adaptive robust control Networked control systems Nonlinear systems State and input quantization Uncertain systems Control and Systems Engineering Signal Processing Hardware and Architecture Computer Networks and Communications Electrical and Electronic Engineering