基于支持向量机的非线性模型预测控制  

Support Vector Machine Based Nonlinear Model Predictive Control

作　　者：张浩然[1] 韩正之[1] 李昌刚[1] 

机构地区：[1]上海交通大学自动化系,上海200030

出　　处：《系统工程与电子技术》2003年第3期330-334,共5页Systems Engineering and Electronics

摘　　要：支持向量机是基于统计学习理论的新一代机器学习技术。由于使用结构风险最小化原则代替经验风险最小化原则,使它较好地解决了小样本情况下的学习问题。又由于采用了核函数思想,使它把非线性问题转化为线性问题来解决,降低了算法的复杂度。提出了一种基于支持向量机的模型预测控制结构,并使用一个新的随机搜索优化算法来求解预测控制律,计算机仿真证明了所设计的控制算法的正确性和有效性。Support vector machines(SVM)are a new-generation machine learning technique based on the statistical learning theory. They can solve small-sample learning problems better by using Structural Risk Minimization in place of Experiential Risk Minimization. Moreover, SVMs can change a nonlinear learning problem in to a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea. A nonlinear predictive control framework is presented, in which nonlinear plants are modeled on a support vector machine. The predictive control law is derived by a new stochastic search optimization algorithm. At last a simulation example is given to demonstrate the proposed approach.

关 键 词：非线性模型 支持向量机 模型预测控制 随机搜索 MPC 

分 类 号：O231[理学—运筹学与控制论]