基于L S-SVM 的非线性预测控制技术

探讨了利用最小二乘支持向量机(LS—SVM)进行非线性系统辨识的方法，LS—SVM用等式约束代替传统支持向量机中不等式约束，求解过程从解QP问题变成解一组等式方程，将得到的LS—SVM模型应用到非线性预测控制，提出了基于LS—SVM模型的非线性预测控制算法，通过CSTR过程仿真表明，最小二乘支持向量机学习速度快，在小样本情况下具有良好的非线性建模和泛化能力，基于LS—SVM的预测控制算法具有很好的控制性能。

Nonlinear predictive control based on LS-SVM

An identification method of nonlinear systems using least squares support vector machine(LS-SVM) is proposed. The constraints of inequalities in the classical SVM approach are replaced by equality-type constraints in LS-SVM. The LS-SVM solution follows directly from solving a set of linear equations instead of quadratic programming. A kind of nonlinear predictive control scheme based on the LS-SVM model is presented. Simulation results for a CSTR process show that LS-SVM can be trained fastly. The LS-SVM has good ability of modeling nonlinear process and good generalization under small data set available. The nonlinear predictive control strategy based on LS-SVM model shows satisfactory performance.