STABLE ADAPTIVE TRACKING OF UNCERTAIN SYSTEMS USING NONLINEARLY PARAMETRIZED ONLINE APPROXIMATORS

The design of stable adaptive neural controllers for uncertain nonline ar dynamical systems with unknown nonlinearities is considered. The Ly apunov synthesis approach is used to develop state-feedback adaptive c ontrol schemes based on a general class of nonlinearly parametrized on -line approximation models. The key assumptions are that the system un certainty satisfies a strict feedback condition and that the network r econstruction error and higher-order terms of the on-line approximator (with respect to the network weights) satisfy certain bounding condit ions. An adaptive bounding design is used to show that the overall neu ral control system guarantees semi-global uniform ultimate boundedness within a neighbourhood of zero tracking error. The theoretical result s are illustrated through a simulation example.