基于粒子群算法的永磁同步电机模型预测控制权重系数设计

doi:10.19595/j.cnki.1000-6753.tces.200752

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Abstract In this paper, a dynamic recombined multi-population particle swarm optimization algorithm based on chaotic-mutation (CDMSPSO) is proposed to realize self-tuning of the weighting factors when model predictive control algorithm (MPC) is dealing with multi-objective and multi-constraint conditions. By analyzing the design principle of cost function in the model predictive torque control (MPTC), taking the root mean square of the current error in the two-phase rotating coordinate system as a reference, the objective function of particles in particle swarm optimization is designed with reducing the torque ripple and reducing the current total harmonic distortion (THD) as the main control objectives. The whole population was divided into several small sub-particle swarms by using CDMSPSO, and the particles were randomly recombined with a certain recombination period, then a random sub-particle swarm is selected and chaotic sequence is generated iteratively on the basis of any particle, and the selected sub-particle swarm is replaced by the new chaotic sequence to realize chaotic mutation of particles. Simulation and experimental results show that this method can solve the problem of weighting factors setting well and achieve excellent steady-state performance.

Key words： Permanent magnet synchronous motor model predictive control weighting factors particle swarm optimization dynamic recombination chaotic mutation

Received: 30 June 2020

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TM341

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200752 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I1/50

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