Observer-based adaptive neural control for nonlinear systems

Based on the Lyapunov synthesis approach, many adaptive neural control schemes have been developed during the last few years. So far, most of these schemes have been applied to the classes of uncertain systems whose state variables are assumed to be measurable. This paper develops an adaptive neural control approach that relaxes the restrictive assumption that is usually made by designing a state observer. The overall adaptive neural control scheme is shown to guarantee the stability of the closed-loop system and obtain good tracking performance as well.