无刷直流电机的RBF神经网络自适应控制研究

针对传统PID控制器在无刷直流电机控制时的鲁棒性差、精度低等缺点,在分析BLDCM数学模型的基础上,设计了RFBNN自适应PID控制器应用于无刷直流电机控制系统。通过Matlab/Simulink环境下的仿真实验表明,与传统的PID控制方法相比,该方法大大改善了系统的动态特性,减小了系统的稳态误差,提高了系统的自适应能力和抗干扰能力,满足了系统的控制性能要求。

Research on RBFNN self-adaptive PID controller for brushless DC motor control system

Aiming at the weakness that traditional PID controller can′ t adjust themselves according to parameter variation of BLDCM, based on the mathematical model of the brushless DC motor, a control strategy which is based on Radial Basis Function Neural Network is provided for BLDCM control system. The experiment result which is based on the environment of Matlab/Simulink show that this control strategy has better dynamic and static characteristics than traditional PID control strategy, reducing the steady- state error of the system and improve the adaptive capacity and anti-jamming, meet performance requirements.