基于模糊系数修正BP神经网络PID的BLDCM控制系统仿真研究

常规PID、模糊算法无法解决无刷直流电机（BLDCM）控制系统存在的强耦合、非线性等问题，在干扰作用下容易出现信号失真。针对该问题，在滑行灯伺服转向系统中，以BLDCM三闭环控制系统为研究对象，结合BP神经网络、模糊控制和PID算法，提出一种基于模糊系数修正BP神经网络的PID控制。通过Simulink建模及仿真，对比研究了该策略与常规控制算法在转矩扰动和磁通扰动状况下的动态响应特性。仿真结果显示该改进控制算法在BLDCM位置控制系统中性能优良。

Simulation Research on BLDCM Control System Based on Fuzzy Coefficient Modified BP Neural Network PID

Conventional PID and fuzzy algorithms have the problems of strong coupling and nonlinearity in the brushless DC motor (BLDCM) control system, and signal distortion is prone to appear under the action of interference. In order to solve these problems, the taxi light servo steering system is taken as the application background, the BLDCM three closed loop control system is taken as the research object, BP neural network, fuzzy control and PID algorithm are combined, and a kind of PID control based on fuzzy parameter modified BP neural network is proposed. Through Simulink modeling and simulation, the dynamic response characteristics of this strategy and conventional control algorithms under torque disturbance and magnetic flux disturbance are compared. The simulation results show that this improved control algorithm has excellent performance in the BLDCM position control system.