摘要
为解决自抗扰控制器(active disturbance rejection control,ADRC)中参数较多且难以整定的问题,提出一种基于LM算法且网络结构可在线优化的径向基函数(radial basis function,RBF)神经网络。利用滑动窗口的思想将在线输入的样本放入一个长度固定的队列,将LM-RBF网络应用于ADRC,在线整定控制器参数,并以永磁同步电机为对象在Matlab里进行仿真分析。结果表明:与基于RBF的常规自抗扰控制器相比,改进后LM-RBF使控制器有更快的响应速度及更优的抗干扰能力,能有效提高被控系统的稳定性,满足非线性时变系统对自抗扰控制器的性能要求。
In order to solve the problem of many parameters and difficult to set in the active disturbance rejection control(ADRC),a radial basis function(RBF)neural network based on LM algorithm and online optimization of network structure is proposed.Using the idea of sliding window,the online input samples are put into a fixed-length queue,the LM-RBF network is applied to ADRC,the controller parameters are set online,and the permanent magnet synchronous motor is used as the object for simulation analysis in Matlab.The results show that compared with the RBF-based conventional active disturbance rejection controller,the improved LM-RBF enables the controller to have faster response speed and better anti-interference ability,which can effectively improve the stability of the controlled system and meet the performance requirements of the nonlinear time-varying system for the active disturbance controller.
作者
唐冲
童仲志
侯远龙
Tang Chong;Tong Zhongzhi;Hou Yuanlong(School of Mechanical Engineering,Nanjing University of Science&Technology,Nanjing 210000,China)
出处
《兵工自动化》
2020年第7期11-15,28,共6页
Ordnance Industry Automation
关键词
LM算法
RBF神经网络
在线整定
自抗扰控制器
LM algorithm
RBF neural network
online setting
active disturbance rejection controller
