Observer-based adaptive control for switched nonlinear systems with input quantization

Abstract

This paper addresses the tracking problem for a class of nonlinear switched system with quantized input via adaptive neural method. The system is described by a set of nonlinear functions which satisfy Lipschitz conditions. A switched nonlinear observer is set up to estimate those unmeasurable state variables. Convex combination method is utilized to determine the observer gain matrix so that the effect from those nonlinear terms can be well compensated for. Then observer-based backstepping method is adopted to construct the quantized input controller. It is also proven that the tracking error converges to a small neighbourhood around the original point under the action of the suggested controller. Finally, a simulation example is studied to test the efficacy of the suggested control strategies.