改进型LM-RBF 神经网络在自抗扰控制器上的应用

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。

Application of Improved LM-RBF Neural Network in ADRC

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.