某交流伺服系统的神经网络自抗扰控制

Improved ADRC of AC Servo System Based on Neural Network

针对采用永磁同步电机(PMSM)驱动的某车载稳定平台交流伺服系统存在摩擦力矩、惯性力矩、负载扰动等一系列复杂非线性问题,考虑到自抗扰控制的抗扰能力强和BP神经网络的自我学习能力强的特点,设计了一种BP神经网络改进型自抗扰控制器(BPNN-ADRC)。为了简化自抗扰控制耗时费力的参数整定过程,采用BP神经网络对自抗扰控制器中的重要参数进行在线整定;针对BP神经网络收敛速度慢、易陷入局部最优的缺陷,引入遗传算法对其初始连接节点的权重进行在线寻优,以期进一步提高系统的控制精度。仿真实验结果显示:该控制策略能有效提升系统的抗干扰能力,为提高车载稳定平台伺服系统的控制性能提出了一种可行的方案。

There are a series of complex nonlinear problems such as friction torque,inertia moment and load disturbances in the AC servo system of a vehicle-mounted stable platform driven by a Permanent Magnet Synchronous Motor(PMSM).In view of the strong anti-interference ability of Active Disturbance Rejection Control(ADRC)and the strong self-learning ability of BP Neural Network(BPNN),an Active Disturbance Rejection Controller improved by BPNN(BPNN-ADRC)is designed.In order to simplify the process of parameter tuning of ADRC,which is time-consuming and laborious,the BPNN is used to tune the important parameters of the ADRC controller online.In order to overcome the defects of slow convergence of the BPNN and its tendency of falling into local optimum,the genetic algorithm is introduced to perform online optimization of the weights of the initially connected nodes,so as to further improve the control accuracy of the system.The results of simulation experiment show that the control strategy can effectively improve the anti-interference ability of the system,which is a feasible scheme for improving the control performance of the servo system of the vehicle-mounted stable platform.