摘要：本文以永磁同步电机为研究对象，针对其非线性、强耦合、多变量的特性导致在使用常规的PID对其控制时难以达到理想的效果，提出了一种基于径向基（RBF）神经网络的分数阶PIαDβ控制策略。利用径向基神经网络的自学习和自训练的功能，对控制器的参数进行在线优化，以便使控制器在未知的系统中能够具有快速的适应能力和较好的控制性能。将设计的控制器应用于永磁同步电机的速度环路中,并在高速度,大负载扰动的条件下对其进行仿真实验。结果表明，使用了RBF神经网络分数阶PIαDβ控制器的电机控制系统，具有良好的快速响应能力和较强的扰动抑制能力。
关键词：永磁同步电机；RBF神经网络；分数阶PIαDβ控制器；速度控制

Abstract: This paper took permanent magnet synchronous motor as research object, proposed a fractionalorder PIαDβ control strategy based on radial basis function (RBF) neural network for its nonlinear, strong coupling and multivariable characteristics, which led to the difficulty of achieving the desired effect when using conventional PID control. The parameters of the controller were optimized online by using the selflearning and selftraining functions of radial basis function neural network, so that the controller can have the fast adaptability and good control performance in an unknown system. The designed controller was applied to the speed loop of permanent magnet synchronous motor , and the simulation experiment was carried out under the conditions of high speed and large load disturbance. The results show that the motor control system with RBF neural network fractional order PIαDβ controller has good fast response capability and strong ability of disturbance rejection.
Key words: permanent magnet synchronous motor; RBF neural network; fractional order PIαDβ controller; speed control