Variable-structure control for linear synchronous motor using recurrent fuzzy neural network

A newly designed variable-structure controller using recurrent fuzzy neural network (RFNN) to control the mover position of a pennant magnet linear synchronous motor (PMLSM) servo drive is developed in this study. First, a variable-structure adaptive (VSA) controller is adopted to control the mover position of the PMLSM where a simple adaptive algorithm is utilized to estimate the uncertainty bounds. Then, to further improve the rate of convergence of the estimation, a variable-structure controller using RFNN is investigated, in which the RFNN is utilized to estimate the lumped uncertainty real-time. Simulated and experimental results show that the proposed variable-structure controller using RFNN provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external disturbance. Furthermore, comparing with the VSA controller, smaller control effort is resulted and the chattering phenomenon is reduced by the proposed variable-structure controller using RFNN.