To overcome the limitations that active disturbance rejection control (ADRC) system of a bearingless induction motor (BIM) has difficulty in tuning parameters depending on experience to select parameters, an ADRC strategy based on improved particle swarm optimization-genetic algorithm (IPSO-GA) is proposed. Based on the orientation of the air-gap magnetic field, the first-order and the second-order ADRC are, respectively, designed for the BIM rotation and suspension parts according to the different order of the BIM system. Then, the parameters of the basic particle swarm optimization algorithm are optimized by considering the characteristics of the basic particle swarm algorithm parameters, and the crossover and mutation operations are introduced to enhance global search capability. Meanwhile, through the performance test based on the test function, the performance of IPSO-GA is verified, and the parameters of ADRC are adjusted by IPSO-GA. In addition, this strategy is analyzed with simulation in MATLAB/Simulink and verified on an experimental prototype. Both simulation and experimental results show that the proposed strategy not only effectively improves the starting performance and antidisturbance ability of the BIM but also reduces the maximum radial offset of the rotor and improves the suspension precision of the motor.

中文翻译：

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基于改进粒子群优化遗传算法的无轴承异步电机自抗扰控制研究


针对无轴承感应电机（BIM）自抗扰控制（ADRC）系统难以依靠经验选择参数进行参数整定的局限性，提出一种基于改进粒子群优化遗传算法（IPSO-GA）的ADRC策略被提议。基于气隙磁场的方向，根据BIM系统的不同阶数，分别为BIM旋转和悬挂部分设计一阶和二阶ADRC。然后，结合基本粒子群算法参数的特点，对基本粒子群算法的参数进行优化，并引入交叉和变异操作来增强全局搜索能力。同时，通过基于测试函数的性能测试，验证了IPSO-GA的性能，并通过IPSO-GA调整了ADRC的参数。此外，该策略在 MATLAB/Simulink 中进行了仿真分析，并在实验原型上进行了验证。仿真和实验结果表明，该策略不仅有效提高了BIM的启动性能和抗干扰能力，而且减小了转子的最大径向偏移，提高了电机的悬挂精度。