基于模糊BP神经网络的智能轮椅BLDCM控制

Intelligent Wheelchair BLDCM Control Based on Fuzzy BP Neural Network

现阶段多数轮椅电机仍使用传统PID控制,该控制方式存在控制精准度较低、超调量较大以及抗扰动能力差等问题。为解决以上问题,通过对无刷直流电机进行研究,在分析了其控制方法后,提出一种基于模糊BP神经网络的BLDCM控制方法。首先,研究了BLDCM结构并搭建数学模型。其次,在模型基础上构建了模糊BP神经网络PID控制器。最后,在Matlab/Simulink中搭建整个电机控制系统进行三种不同工况下的运动控制仿真,并与传统PID控制算法进行对比。实验结果表明:模糊BP神经网络PID控制策略能获得更好的PID控制参数,具有良好的抗扰动能力,有效的改善了整个轮椅控制系统的动态性能。

At present,most wheelchair motors still use traditional PID control,which has problems such as low control accuracy,large overshoot and poor anti-disturbance ability.To solve these problems,a fuzzy BP neural network-based control method for Brushless Direct Current Motors(BLDCM)was proposed after studying its control methods.Firstly,the BLDCM structure was studied and a mathematical model was constructed.Secondly,a fuzzy BP neural network PID controller was constructed on the basis of the model.Finally,the whole motor control system was built in Matlab/Simulink to simulate the motion control under three different working conditions,and compared with the traditional PID control algorithm.The experimental results show that the fuzzy BP neural network PID control strategy can obtain better PID control parameters,have good anti-disturbance ability,and effectively improve the dynamic performance of the entire wheelchair control system.