一种基于LS-SVM与PID复合的逆控制系统  

A inverse control system based on least squares support vector machine and PID

作　　者：黄银蓉[1] 张绍德[1] 季民[1] 

机构地区：[1]安徽工业大学电气信息学院,安徽马鞍山243002

出　　处：《机电工程》2010年第2期75-78,共4页Journal of Mechanical & Electrical Engineering

摘　　要：针对逆系统中非线性逆模型辨识困难的问题,研究了基于最小二乘支持向量机(LS-SVM)的逆模型辨识及控制,并用微粒子群算法(PSO)优化LS-SVM的参数和核函数参数。提出了一种由LS-SVM的逆模型与PID结合的复合控制系统,由LS-SVM辨识非线性系统的逆模型作为前馈控制器,形成直接逆控制。同时,由PID控制器构成反馈控制,克服直接逆控制鲁棒性不强的缺陷。仿真研究结果表明LS-SVM的逆模型辨识能力强,该复合控制系统具有比基于最近邻聚类的RBF神经网络逆控制系统更优的动态跟踪性能,更好的抗干扰能力和鲁棒性。Aiming at the problems of the inverse model identification in inverse system method,the realization of inverse system identification and control using least squares support vector machine（LS-SVM） were studied.A methodology,based on particle swarm optimization algorithm（PSO）,for parameters selection of least squares support vector machine was proposed.A compound control strategy combing LS-SVM inverse system with PID controller was proposed.LS-SVM was used to identify the inverse model of nonlinear system,and this inverse model was used as feed-forward controller to design direct inverse control.Moreover,PID controller was used to realize feed-back control,which could overcome the limits of direct inverse control in performance and robustness.Simulation results demonstrate that LS-SVM has a good approximate capability for inverse model,and the proposed compound control system has better dynamic track performance,resistance to disturbance of system and robustness than RBF neural network.

关 键 词：逆模型辨识 最小二乘支持向量机 微粒子群算法 逆控制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]