Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation

Abstract EN

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation.

The unknown smooth nonlinear functions are approximated directly by neural networks.

A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process.

Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability.

In the end, the effectiveness of the proposed method is verified by a simulation example.