摘要
对于非线性系统预测控制问题,本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值,通过输出反馈和偏差校正减少预测误差,由PSO滚动优化获得非线性系统的控制量.该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器.通过对单变量多变量非线性系统进行仿真,证明了该预测控制方法是有效的,且具有良好的自适应能力和鲁棒性.
For the predictive control of nonlinear systems, we present a single-step predictive control algorithm based on model learning and particle swarm optimization（PSO）. The method utilizes least square support vector machine（LSSVM） to estimate the model of a nonlinear system and forecast the output value, reducing the error in output feedback and error correction. The control values are obtained by the rolling optimization of PSO. This method can be used to design effective controllers for nonlinear systems with unknown mathematical models. For univariate and multivariate nonlinear systems, simulation results show that the predictive control algorithm is effective and has an excellent adaptive ability and robustness.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2010年第2期164-168,共5页
Control Theory & Applications
基金
国家自然科学基金资助项目(60874013)
教育部新世纪优秀人才支持计划资助项目(NCET-08-0106)
教育部博士点基金资助项目(200802860039)
关键词
非线性系统
预测控制
最小二乘支持向量机
粒子群
nonlinear systems
predictive control
least square support vector machine
particle swarm optimization
