Journal ArticleDOI
Iterative learning control for discrete-time systems with exponential rate of convergence
N. Amann,David H. Owens,Eric Rogers +2 more
- Vol. 143, Iss: 2, pp 217-224
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TLDR
An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms and has potential benefits which include realisation in terms of Riccati feedback and feedforward components.Abstract:
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.read more
Citations
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Journal ArticleDOI
A survey of iterative learning control
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Linear systems
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Journal ArticleDOI
Iterative Learning Control: Brief Survey and Categorization
TL;DR: The iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors.
Journal ArticleDOI
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
TL;DR: It is shown that, within the framework of the quadratic-criterion-based ILC (Q-ILC), various practical issues such as constraints, disturbances, measurement noises, and model errors can be considered in a rigorous and systematic manner.
Book ChapterDOI
Iterative Learning Control: An Expository Overview
TL;DR: This chapter gives an overview of the field of iterative learning control (ILC), followed by a detailed description of the ILC technique, followed by two illustrative examples that give a flavor of the nature of ILC algorithms and their performance.
References
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Proceedings ArticleDOI
Non-minimum phase plants in iterative learning control
N. Amann,D.H. Owens +1 more
TL;DR: In this paper, the problem of iterative learning control with non-minimum phase plants is studied and one specific algorithm (norm-optimal iterative control) and one particular plant structure (a stable, SISO, nonminimum phase plant) are considered exclusively.
Proceedings ArticleDOI
Iterative learning control-Convergence using high gain feedback
TL;DR: In this article, a theoretical approach for a class of linear systems in state-space form is presented for iterative learning control systems combining aspects of current Iterative Learning theory with control-theoretical techniques to provide a well defined convergence criterion parameterized by a single gain parameter.
Proceedings ArticleDOI
Optimal iterative learning control of an extrusion plant
K. Buchheit,M. Pandit,M. Befort +2 more
TL;DR: In this article, the authors derived a method for iterative learning control, in which the sequence of input functions iteratively minimizes a functional chosen to represent system performance, based on the concept of the gradient of a functional which is expressed in terms of the Frechet differential.