Abstract

This paper focuses on chaos control of the micro-electro-mechanical system with unknown system parameters and dead-zone input existing in the engineering application. The phase diagrams, corresponding time histories and bifurcation diagram are employed to reveal the chaotic dynamics performance of the micro-electro-mechanical system. For eliminating chaos and vibration, an adaptive neural-network-based dynamic surface control is proposed to convert the chaos motion into regular motion without imposing any condition on parameters of system model and the boundedness of control gain. Meanwhile, to achieve high accuracy and quick response, a neural network is employed to approximate unknown nonlinear item of model and an adaptive law is designed to estimate unknown control gain in the framework of dynamic surface control. Finally, some simulations are executed and corresponding results show effectiveness and robustness of the proposed scheme.