Adaptive NN Tracking Control with Prespecified Accuracy for a Class of Uncertain Periodically Time-Varying and Nonlinearly Parameterized Switching Systems

Abstract This paper focuses on a tracking control issue for a class of uncertain switching nonlinear systems under arbitrary switching with unknown time-varying parameters. An approximator is designed by combining Multilayer neural network (MNN) with Fourier series expansion (FSE) to approximate the nonlinearly parameterized unknown functions. Then, a common Lyapunov function (CLF) is constructed and a novel adaptive neural network (NN) control scheme is proposed by using the adaptive Backstepping technique. It can be proven that the tracking error falls into a prespecified domain of equilibrium and the whole system is semi-globally uniformly ultimately bounded (SGUUB). The effectiveness of the designed approach is verified through two simulation examples.