Quantized-State-Feedback-Based Neural Control for a Class of Switched Nonlinear Systems With Unknown Control Directions

Abstract

This paper investigates the problem of unknown virtual control directions in a state-quantized adaptive recursive control design for a class of arbitrarily switched uncertain pure-feedback nonlinear systems in a band-limited network. State quantization is considered for state feedback control in a bandlimited network. The primary contribution of this study is to provide a quantized state feedback adaptive control strategy to address the unknown control direction and arbitrarily switched nonaffine nonlinearities. Herein, a coupling problem between Nussbaum functions and quantization errors caused by quantized state feedback control laws is considered in the Lyapunov-based design and stability analysis. A statequantized adaptive recursive control scheme using the function approximation is constructed without a priori knowledge of the signs of the control gain functions, where the estimated parameters and Nussbaumtype functions are adaptively updated via quantized states. Theoretical lemmas are derived to show that the adaptive parameters and quantization errors of the closed-loop signals are bounded using the proposed control scheme. The boundedness of the closed-loop signals and the convergence of tracking error to a neighborhood of the origin are proved using the common Lyapunov function approach. Two simulation examples are shown to illustrate the effectiveness of the proposed theoretical result.

Neural-Networks-Based Adaptive Fault-Tolerant Control of Nonlinear Systems With Actuator Faults and Input Quantization

“…It is a fact that quantization occurs frequently in modern applications [29]. Since then, research into the control problem of nonlinear systems with quantization has shown to be fruitful, and a number of efficient strategies have been investigated [30]- [34]. A class of uncertain switched nonlinear systems in strict feedback form has been studied in [35] to examine the challenge of adaptive fuzzy quantized output-feedback control and a hysteretic quantizers for switched nonlinear uncertain systems have recently been studied in [36].…”