Adaptive Neural Backstepping for a Class of Switched Nonlinear System Without Strict-Feedback Form

Abstract

This paper focuses on backstepping-based adaptive neural control for switched nonlinear systems in nonstrict-feedback form. A structural characteristic of radial basis function neural networks is first developed. With this structural characteristic, adaptive neural backstepping has been extended to the switched nonlinear systems with nonstrict-feedback structure. By using a common Lyapunov function method, an adaptive neural controller is constructed by backstepping technique. It is shown that under the action of the suggested controller, all the closed-loop signals are bounded and meanwhile the system output follows the desired reference signal well. Finally, a numerical simulation example is used to illustrate the effectiveness of our results.