Iterative learning control for discrete-time systems with exponential rate of convergence
Iterative learning control for discrete-time systems with exponential rate of convergence
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DOI：
10.1049/ip-cta:19960244
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发表时间：
1996-03-01
期刊：
影响因子：
--
通讯作者：
Rogers, E
中科院分区：
文献类型：
--
作者：
Amann, N;Owens, DH;Rogers, E
基于其他作者用来推导梯度型算法的优化原理，提出了一种迭代学习控制算法。新算法是一种下降算法，具有潜在的好处，包括实现 Riccati 反馈和前馈组件。这种实现还具有隐式确保自动步长选择的优点，从而保证收敛，而不需要根据经验选择参数。该算法实现了可逆植物的几何收敛速度。该算法的一个重要特征是收敛速度依赖于为优化问题选择的信号范数中出现的权重参数。
An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realisation in terms of Riccati feedback and feedforward components. This realisation also has the advantage of implicitly ensuring automatic step-size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants. One important feature of the proposed algorithm is the dependence of the speed of convergence on weight parameters appearing in the norms of the signals chosen for the optimisation problem.