Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems
Yan GENG
Xiaoe RUAN
摘要：In this paper, a quasi-Newton-type optimized iterative learning control (ILC) algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.
机标关键词：iterative learning controlsuperlinear convergencemonotone convergencelearning algorithmtracking errorlearning lawinstead ofrange of
资助基金：the National Natural Science Foundation of China ()the National Natural Science Foundation of China ( F010114-60974140)the National Natural Science Foundation of China (61273135)
论文发表日期：2015-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 256-265 )
英文信息