中文摘要：

在这份报纸， quasi-Newton-type 优化了控制(ILC ) 算法为分离线性时间不变的系统的一个类被调查的反复的学习。建议学习算法是由一个 quasi-Newton-type 矩阵更新学习获得矩阵而不是植物的倒置。借助于数学归纳方法，建议算法的单调集中被分析，它证明追踪的错误单调地在重复的一个有限数字以后收敛到零。与存在相比优化了 ILC 算法，由于伪的 superlinear 集中 -- 牛顿方法，建议学习法律与更快的会聚的率操作并且对柔韧系统模型有病条件，并且因此拥有大量应用。数字模拟表明有效性和有效性。

英文摘要：

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.