摘要
将现有的面向单输入单输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(SISO-LSSVM-LPV),推广到多输入多输出系统,实现了面向多输入多输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(MIMO-LSSVM-LPV),进一步结合基于遗传算法的预测控制算法(GA-MPC),提出并实现了MIMO-LSSVM-LPV+GA-MPC的建模控制一体化新架构。仿真结果表明,该辨识算法可逼近复杂非线性MIMO系统,辨识精度高,并且保留了线性回归低计算量的优点,结合了GA的MPC可实现最优控制量的在线实时寻优,并取得了良好控制效果。
This paper presents a least-square support vector machine based linear parameter-varying model approach for multiple-input multiple-output nonlinear system （MIMO-LSSVM-LPV）. The identified model can be used in the model predictive control scheme combined with genetic algorithm （GA-MPC）. The new identification and controlling integration scheme is named MIMO-LSSVM-LPV＋GA-MPC. Simulation results show that the identification algorithm can approximate complex nonlinearity with high accuracy while keep the advantage of low computational burden of linear regression. GA based MPC can get the real-time optimal control input and achieve good controlling performance.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2015年第1期197-205,共9页
CIESC Journal
基金
国家重点基础研究发展计划项目(2012CB720500)
国家自然科学基金项目(21076179)~~
