基于PSO滚动优化的LS-SVM预测控制

Predictive controlling of LS-SVM based on rolling optimization by PSO

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.