Sliding-mode-controlled slider-crank mechanism with fuzzy neural network

The dynamic response of a sliding-mode-controlled slider-crank mechanism, w hich is driven by a permanent-magnet (PM) synchronous servo motor, is studi ed in this paper. First, a position controller is developed based on the pr inciples of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which an FNN is adopted to adjust the control gain in a switching control law on lin e to satisfy the sliding mode condition. In addition, to guarantee the conv ergence of tracking error, analytical methods based on a discrete-type Lyap unov function are proposed to determine the varied learning rates of the FN N. Numerical and experimental results show that the dynamic behaviors of th e proposed controller-motor-mechanism system are robust with regard to para metric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller.