Adaptive neural quantized control for a class of switched nonlinear systems

Abstract

This paper addresses neural adaptive quantized control for switched nonlinear non-strict feedback systems. The systems under consideration have unknown virtual control coefficients, and the system states are unmeasurable. Another feature of the systems is that they have quantized input and output signals. The control objective is first to set up a robust switched observer, and then an adaptive neural tracking control strategy is proposed via estimated state feedback. By Lyapunov stability theory, we show that the constructed adaptive neural controller guarantees a small tracking error and the boundedness of all the closed-loop signals, despite the fact that the systems contain unknown virtual control coefficients, and quantized input and output signals. Eventually, two examples are employed to demonstrate the validity of our results.