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10 November 2022 Research on motor model predictive control strategy based on speed loop optimization
Zhiquan Li, Mengda Li, Lijiao Li, Shuyi Li
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Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 1230106 (2022) https://doi.org/10.1117/12.2644455
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China
Abstract
In order to improve the problem of slow torque and speed response of permanent magnet synchronous motor, a strategy combining model predictive control and particle swarm optimization is proposed in this paper. By analyzing the error of the input parameters in the model predictive control system, the Particle Swarm Optimization (PSO) was introduced to improve the speed loop. When the system model is changed, the particle swarm algorithm is repeatedly iterated to obtain better PI parameters, thereby improving the given current accuracy of the model predictive control system and improving the stability of the system operation. The simulation results show that this method shortens the adjustment time, improves the running stability of the permanent magnet synchronous motor, and reduces the torque ripple problem and the steady-state error of the system.
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Zhiquan Li, Mengda Li, Lijiao Li, and Shuyi Li "Research on motor model predictive control strategy based on speed loop optimization", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 1230106 (10 November 2022); https://doi.org/10.1117/12.2644455
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KEYWORDS
Control systems
Particle swarm optimization
Systems modeling
Mathematical modeling
Performance modeling
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