Disturbance Rejection via Adaptive Neural Design for a Class of Non-Minimum Phase Nonlinear Systems

In this paper, the problem of disturbance rejection for a class of non-minimum-phase cascaded nonlinear systems with parameter uncertainty is considered. For the purpose of reducing the reservation from robust control method, we develop an adaptive control design approach based on Lyapunov method and neural network theory. Because the radial-basis function networks (RBF NNs) have the good structure and numerical value property, the adaptive controller has good learning ability for the uncertainty. The simulation shows that under a small control gain, the-gain from to is less than a given value.