基于模糊神经网络的永磁同步电机伺服系统研究

永磁同步电机具有非线性、强耦合的特性,常规的矢量控制方法难以对其进行精确控制。此外,电机系统易受负载扰动影响,从而产生转速和电磁转矩波动。针对转速环参数固定会导致系统响应速度慢、超调量大的问题,文中提出了一种模糊径向基神经网络PID控制策略,用以替代矢量控制系统中转速环PID控制。将神经网络和模糊控制相结合,基于增量式PID控制方式,利用梯度下降优化算法动态调整转速环中的PID参数。系统模型仿真结果表明,模糊神经网络PID控制的电机系统超调量较小,相较于常规PID控制,新模型在低速和高速运行的启动时间分别缩短了66.7%和75.9%,动态响应更快,具有更好的鲁棒性和抗干扰能力。利用DSP搭建了实验平台,实验结果也证明了该控制方法的有效性。

Research on Permanent Magnet Synchronous Motor Servo System Based on Fuzzy Neural Network

Permanent magnet synchronous motors have nonlinear and strong coupling characteristics, and it is difficult to accurately control them with conventional vector control methods. Besides, the motor system is susceptible to load disturbances, resulting in speed and electromagnetic torque fluctuations. In view of the problem of slow system response and large overshoot caused by fixed speed loop parameters, this study proposes a fuzzy radial basis function neural network PID control strategy to replace the PID control of speed loop in the vector control system. Based on the incremental PID control method, this strategy combines neural network and fuzzy control, and uses the gradient descent optimization algorithm to dynamically adjust the PID parameters in the speed loop. The simulation results show that the overshoot of the motor system controlled by the fuzzy neural network PID is small. Compared with conventional PID control, the proposed method has reduced the start-up time of low-speed and high-speed operation by 66.7% and 75.9%, respectively, and has faster dynamic response, better robustness and anti-interference ability. The experimental platform is built using DSP, and the experimental results prove the effectiveness of the control method.