基于改进型RBF神经网络多变量系统的PID控制  

PID Control for Multivariable System Based on Ameliorative RBF Neural Networks

作　　者：李绍铭[1] 刘寅虎[1] 

机构地区：[1]安徽工业大学电气信息学院安徽省电力电子与电力传动重点实验室,马鞍山243002

出　　处：《重庆大学学报（自然科学版）》2007年第2期53-57,共5页Journal of Chongqing University

基　　金：安徽省教育厅自然科学基金项目(2006KJ032B)

摘　　要：针对工业控制中多输入多输出非线性时变系统,提出了基于改进型RBF神经网络的智能PID控制方法.采用最近邻聚类算法在线构造RBF神经网络辨识器并在线辨识被控对象,对PID控制器参数进行在线调整,实现了多变量非线性时变系统的解耦控制.仿真结果表明,控制器能根据系统运行状态获得对应于某种最优控制规律下的PID参数,解耦后的系统具有较好的动态和静态性能,与常规RBF神经网络PID控制方法相比,该方法具有控制精度高、响应速度快的优点,并且具备较强的自适应性和鲁棒性.Aiming at systems which are of characteristics of multi-input and multi-output, nonlinearity and time-variation in the industrial control fields, this paper presents a intelligent PID control method based on ameliorative RBF neural networks, which constructs RBF neural networks identifier on-line and identifies a controlled object on-line by means of adopting the nearest neighbor-clustering algorithm, and adjusts parameters of PID controller on-line and realizes decoupiing control of multivariable, nonlinear and time-variation system. The simulation result indicates that the controller can get parameters which are optimal under some control law, it makes the decoupled system, compared to the PID control method based on the conventional RBF neural networks, has perfect dynamic and static performances, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.

关 键 词：改进型RBF神经网络 非线性时变系 PID控制 最近邻聚类算法 解耦控制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]