基于支持向量机的非线性系统模型预测控制

Support Vector Machine Based Nonlinear Model Predictive Control

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,使它较好的解决了小样本情况下的学习问题。又由于其采用了核函数思想,使它把非线性问题转化为线性问题来解决,降低了算法的难度,具有全局最优、良好泛化能力等优越性能,得到广泛的研究。基于上述特性提出了一种基于支持向量机的非线性模型预测控制结构,其中使用遗传算法来求解预测控制律,随后用计算机仿真证明了此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 learmng theory. They can SOlVe small - sample learning problems better by using Structural Risk Minimization in place of Experiential Risk Minimization. Moreover, SVM can change a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea. They have recently attracted growing research interest due to their obvious advantage such as good generalization ability, unique and globally optimal SOlutions. Based on the characteristics of SVM 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 genetic algorithm. At last a simulation example is given to demonstrate the proposed approach.