基于单神经元自适应PID的SRM直接转矩控制

Direct Torque Control for SRM Based on Single Neuron Adaptive PID

针对开关磁阻电机转矩脉动大、非线性严重和数学模型难建立等问题,将单神经元自适应PID控制和传统PI控制相结合,构成了开关磁阻电机直接转矩控制系统。根据预设定的误差限,利用神经元自学习能力消除系统较大的误差信号,小误差信号通过传统PI进行消除,使得系统能够更好的适应各种扰动。最后,在Matlab/Simulink环境下,对四相开关磁阻电机进行了仿真,仿真结果表明,复合控制方法能够有效的减小转矩脉动,提高系统的动、静态性能。

For large torque ripple, serious nonlinear problem and the mathematical model is difficult to establish, the direct torque control system was design for switched reluctance motor, which combined single neuron adaptive PID control with the traditional PI control. According to the error limit, the self-learning ability of neurons was used to eliminate a large error signal, and the small error signal was eliminated by the traditional PI control. The simulation for the four-phase switched reluctance motor was done in the Matlab/ Simulink environment. The simulation results show that control strategy can effectively suppress torque fluc- tuation and obtain good system characteristics.