Neural adaptive control of uncertain chaotic systems with input and output saturation

摘要

This paper is concerned with the neural adaptive control design problem of a class of chaotic systems with uncertain dynamics, input and output saturation. To attenuate the effect caused by input and output saturation, a constructed auxiliary system is used to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks are used in online approximation of the uncertain dynamics. Both state feedback and output feedback control laws are designed. In the output feedback situation, a high-order sliding-mode observer is used to estimate the system states. The stability of closed loop system is proved rigorously based on Lyapunov theorem. The effectiveness of the proposed methods is demonstrated by controlling Duffing system and Genesio system.