Yunru Jia

1 Introduction In nonlinear control systems, functional uncertainties, unmodeled dynamics, and external disturbances often degrade tracking accuracy and convergence performance. Owing to the universal approximation capability of neural networks, they have been widely used to approximate unknown nonlinear functions, and many neural-network-based control methods have been developed for uncertain nonlinear systems [1-6].