Position control of PMLSM based on TSKtype recurrent fuzzy neural network
                                                                    XIONG Yuanlin1, FANG Baoying2
(1. Electrical Engineering Department, Jiangsu Maritime Institute,Nanjing 211170，China; 2. School of OpticalElectrical and Computer Engineering，University of Shanghai for Science and Technology, Shanghai 200093，China)

Abstract: In order to solve the problem of position precision control of permanent magnet linear synchronous motor (PMLSM) servo system based on fieldoriented control, a TSKtype recurrent fuzzy neural network (TSKRFNN) control method was proposed. Considering that system was susceptible to uncertainties such as parameter changes, external disturbances and frictions, a PMLSM dynamic mathematical model with uncertainties was established. The TSKRFNN was used to do structure learning and parameter learning of the system at the same time. The system could be automatically increased the neuron resistance to external disturbance and improved the robustness of the system，ensured the dynamic performance of the system. Experimental results show that，compared with the fuzzy neural network type PID, the proposed method can identify the parameters of PMLSM, suppress uncertainties of the system and improve the robust performance and tracking performance of the system effectively.

Key words: permanent magnet linear synchronous motor (PMLSM); uncertainties; TSKtype recurrent fuzzy neural network; robust performance; tracking performance