MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization

• Published in: Control theory and technology 2017-05, Vol.15 (2), p.138-149
• Main Authors: Ren, Bingtao, Chen, Hong, Zhao, Haiyan, Xu, Wei
• Format: Article
• Language: English
• Subjects: Complexity; Computational Intelligence; Computer simulation; Computing time; Control; Control and Systems Theory; Dynamic programming; Electric vehicles; Engineering; Mechatronics; Motors; MPC; Optimal control; Optimization; Permanent magnets; Predictive control; Pruning; Robotics; Sequences; Strategy; Switching; Synchronous motors; Systems Theory; Torque; 优化控制; 最优输入; 模型预测控制; 永磁同步; 电动汽车; 电机转矩; 计算效率
• ISSN: 2095-6983; 2198-0942
• DOI: 10.1007/s11768-017-6193-z

In order to effectively achieve torque demand in electric vehicles （EVs）, this paper presents a torque control strategy based on model predictive control （MPC） for permanent magnet synchronous motor （PMSM） driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming （DP） algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.