Abstract
For the second order nonlinear system, a robust intelligent tracking control scheme is addressed in the presence of system uncertainties. Considering the dynamics with system uncertainties, the robust neural control is designed to obtain robust tracking performance, where a switching mechanism is employed to achieve the coordination between robust design and composite neural learning. To reduce the sliding mode chattering of terminal sliding mode controller (TSMC), the adaptive recursive integral TSMC (ARTSMC) is proposed, where the parameters of ARTSMC are online estimated by updating laws. Furthermore, the proposed method is applied to the dynamics of MEMS gyroscopes and simulations results are presented to verify that more accurate system tracking can be obtained.