摘要
为提高永磁同步电机控制系统抗负载扰动和电机电气参数时变的能力,提出一种基于ADRC通过最小二乘参数辨识的方法,将电机电气参数实时辨识获得电流环内外扰动总和反馈到电流环自抗扰控制器中,并采用径向基神经网络(RBF)方法对非线性扩张观测器(NLESO)参数进行在线整定,获得最优参数,从而对上述扰动进行精确补偿。实验结果表明该方法改进控制器将观测器参数由三个且范围在±106减少为两个参数、调节范围在0~1,当负载扰动由0突增至3.9 N·m时,位置误差波动由原来的41.05%降低至4.21%,同时在小于100 r/min的低速时速度波动由原来的15.52%降低至2.00%;当输入位置角度信号,位置角度误差由原来的4.06%降低至0.89%,进一步提高了电机控制系统位置控制精度和抗扰动能力。
In order to improve the ability of the permanent magnet synchronous motor control system to resist the load disturbance and the time-varying electrical parameters of the motor,a method of least square parameter identification based on ADRC is proposed.In the active disturbance rejection controller,the radial basis neural network(RBF)method is used to tune the nonlinear extended observer(NLESO)parameters online to obtain the optimal parameters,so as to accurately compensate the above disturbances.The experimental results show that the improved controller of this method reduces the observer parameters from three parameters with a range of±106 to two parameters,and the adjustment range is from 0 to 1.When the load disturbance suddenly increases from 0 to 3.9 N·m,the position error fluctuates.The original 41.05%is reduced to 4.21%,and the speed fluctuation is reduced from the original 15.52%to 2.00%at low speed less than 100r/min;when the position angle signal is input,the position angle error is reduced from the original 4.06%to 0.89%,The position control accuracy and anti-disturbance capability of the motor control system are further improved.
作者
白国长
娄轲
马超群
BAI Guo-chang;LOU Ke;MA Chao-qun(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《组合机床与自动化加工技术》
北大核心
2022年第10期74-78,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金资助项目(51775515)。
关键词
自抗扰控制
改进自抗扰控制
递推最小二乘法
径向基神经网络
位置控制
active disturbance rejection control
improved active disturbance rejection control
recursive least squares method
radial basis neural network
position control
