摘要
针对非线性时延系统、传统预测控制算法难以建立精确模型、控制精度不高的现状,提出一种基于最小二乘支持向量机(LS-SVM)的非线性系统预测控制算法。该算法通过LS-SVM对非线性系统输入输出数据序列的训练学习,建立其预测模型;然后运用粒子群(PSO)算法完成非线性预测控制的滚动优化。仿真结果表明,基于该方法的非线性系统预测控制具有较好的控制效果。
It is difficult to build an accurate model using the traditional predictive control algorithm for the nonlinear time delay system, and the control accuracy is low. This paper proposed a nonlinear predictive control algorithm based on least squares support vector machines (LS-SVM) model. It obtained the nonlinear off-line model of the nonlinear system by LS- SVM to train a sequence data of input and output, and finished the whole rolling optimization procedure by PSO. The simula- tion results illustrate that the nonlinear predictive control using LS-SVM is effective.
出处
《计算机应用研究》
CSCD
北大核心
2009年第4期1381-1383,共3页
Application Research of Computers
基金
国家"863"计划重点资助项目(2006AA020301)
关键词
非线性模型预测控制
非线性建模
最小二乘支持向量机
粒子群算法
nonlinear model predictive control
nonlinear modeling
least square SVM
particle swarm optimization (PSO)