Novel Adaptive Inverse Control for Permanent Magnet Synchronous Motor Servo System

The adaptive inverse control is a novel method in control system. It makes set signal, parameter disturbance and external disturbance separately controlled and makes them reach the optimal control without compromiseThe traditional adaptive inverse control system often used FIR filters. It madde the system costing long training time and slow convergence. So, it is unable to adapt the requirement of real-time control system. In this paper, a novel adaptive inverse control with adaptive neuro-fuzzy inference system (ANFIS) was designed for permanent magnet synchronous motor (PMSM) servo system. Meanwhile the microhabitat particle swarm optimization (MPSO) algorithm and RLS algorithm were used for updating parameters for ANFIS. This hybrid learning algorithm can reduce the computing costs and improve the convergence speed. Also, the ANFIS was used to for identification and inverse modeling of PMSM servo system. The simulation results show that the PMSM servo system based on novel adaptive inverse control strategy achieve higher tracking ability, steady precision and good robustness. Streszczenie. Przedstawiono nowy adaptacyjny system sterowania odwrotnego wykorzystujący neuronowy-rozmyty system interferencji ANFIS. System zastosowano do układu serwomechanizmu z silnikiem synchronicznym o magnesach trwałych. Dodatkowo rojowy algorytm optymalizacji oraz algorytm RLS były wykorzystywane do zmiany parametrów. Badania symulacyjne potwierdziły, że nowy system ma dobra precyzję i odporność na zakłócenia. (Nowy adaptacyjny odwrotny system sterowania silnikiem synchronicznym o magnesach trwałych)