Research on Markov Decision Model Predictive Control of Interior Permanent Magnet Synchronous Motor Based on Lumped Disturbances Compensation

Abstract

To improve the performance of the interior permanent magnet synchronous motor control system, a Markov decision model predictive current control strategy based on a lumped disturbances sliding mode disturbance observer is proposed in this paper. A fast terminal sliding mode disturbance observer based on a recursive integral sliding surface is designed to observe and compensate the unideal factors in the motor control system unified as lumped disturbances. Then, according to the characteristic of model predictive control where only the first vector in the optimal control sequence is selected and applied to the system during rolling optimization, the discounted cost criterion based on the Markov decision process is introduced to enhance the control performance of the system. The compensation of lumped disturbances can eliminate the impact of unideal factors, enhance the dynamic performance of the speed loop, and eliminate the static errors in the current loop. The introduction of the discounted cost criterion can reduce the fluctuations in system states without affecting the system’s dynamic performance. Moreover, the proposed control strategy does not require the original control structure of the system to be changed. Experiments are carried out to verify the effectiveness of the proposed method.