STABLE ADAPTIVE NEURAL CONTROL SCHEME FOR NONLINEAR-SYSTEMS

Based on the Lyapunov synthesis approach, several adaptive neural cont rol schemes have been developed during the last few years. So far, the se schemes have been applied only to simple classes of nonlinear syste ms. This paper develops a design methodology that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually m ade. One such assumption is the requirement of a known bound on the ne twork reconstruction error. The overall adaptive scheme is shown to gu arantee semiglobal uniform ultimate boundedness. The proposed feedback control law is a smooth function of the state.