An Iterative Learning Control Algorithm Based on Predictive Model

摘要：

An algorithm of iterative learning control(ILC) based on predictive model is proposed for a kind of repetitive tracking process of the discrete time system with CARMA model. The repetitive tracking process is operated along with the reference trajectory with performance of predictive control based on predictive model of the one step minimum variance. The convergence of this algorithm is analyzed and convergence conditions are derived. The algorithm for linear stable process can be achieved one iteration unbiased tracking for any changing trajectory when the estimation of model parameters is unbiased. In the car suspension system as an example, the simulation results demonstrate this algorithm can achieve fast unbiased tracking for the changing trajectory. It can still achieve unbiased tracking by 4~5 times of iterative learning control while errors of model parameters estimation are changing in ％ 30 ± .

关键词： Iterative learning control Predictive model Adaptive Minimum variance

作者: Zhai Chun-yan Xue Ding-yu Li Ping Li Shu-chen

作者单位: School of Information Science& Engineering, Northeastern University , Shenyang, 110004 School of Inf School of Information Science& Engineering, Northeastern University , Shenyang, 110004 School of Information & Control Engineering, Liaoning Shihua University, Fushun, 113001

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2043-2046

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）