Adaptive and fuzzy neural network sliding-mode controllers for motor-quick-return servomechanism

Abstract

The control performance of an adaptive and a fuzzy neural network (FNN) sliding-mode controlled quick-return mechanism, which is driven by a field-oriented control permanent magnet (PM) synchronous servo motor, is presented in this study. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principle of the sliding-mode control, an adaptive sliding-mode controller is developed to control the slider position of the motor-mechanism coupled system. Moreover, an FNN sliding-mode controller is implemented to control the motor-quick-return servomechanism for comparison. Finally, the effectiveness of the proposed adaptive and FNN sliding-mode controllers are demonstrated by some simulated and experimental results. Compared with the adaptive sliding-mode controller, the FNN sliding-mode controller results much less tracking error with improved control performance.