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Novel Adaptive Inverse Control for Permanent Magnet Synchronous Motor Servo System

Yulin G., Yongyin Q.

Przegląd Elektrotechniczny

2012

R. 88, nr 3b

9-14

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.

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.

Composite Adaptive Inverse Controller Design for Permanent Magnet Synchronous Motor

Qu Y., Gong Y., Cui Y., Han T.

Przegląd Elektrotechniczny

2012

R. 88, nr 7b

365--369

Permanent magnet synchronous motor (PMSM) servo system is a nonlinear, multi-variables strong coupling system. To improve the performance of the PMSM system, a composite adaptive inverse control strategy is proposed. This control strategy adopt improved radial basis function (RBF) neural network and FIR filter as nonlinear filter. The proposed filter is used to identify the system, inverse system and design the adaptive inverse controller. Meanwhile the chaos multi-population particle swarm optimization (CMPSO) algorithm is proposed to training the parameters of the nonlinear filter offline. And then an improved variable step size LMS (IVSLMS) algorithm is used to optimize the parameters online. These algorithm improves the convergence speed and accuracy, further improves the control performance of adaptive inverse control. The results of simulation and experiment indicate that the PMSM servo system has good dynamic, static performance and robustness by using proposed hybrid adaptive inverse control strategy.

W celu poprawy parametrów silnika synchronicznego z magnesami trwałymi PMSM zaproponowano kompozytowa adaptacyjną strategię sterowania. Strategia wykorzystuje sieć neuronową i nieliniowy filtr SOI.