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
Iterative learning in optimal control of linear dynamic processes
TL;DR: A gradient-like learning procedure is developed based on the Frechet derivative of a control quality criterion that proves its convergence and provides local bounds on parameter uncertainty for which the convergence of the learning process is still retained.
Journal ArticleDOI
Iterative Learning Control-Monotonicity and Optimization
David H. Owens,Steve Daley +1 more
TL;DR: This paper concentrates on linear systems and the potential for the use of optimization methods and switching strategies to achieve effective control and introduces some of these issues from the point of view of the research group at Sheffield University.
Journal ArticleDOI
Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model
Zhihua Xiong,Jie Zhang,Jin Dong +2 more
TL;DR: A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, and results show that the performance of trajectory tracking is gradually improved by the ILC.
Iterative Learning Control design for uncertain and time-windowed systems
Jjm Jeroen Wijdeven,van de +1 more
TL;DR: This thesis presents a finite time interval robust ILC control strategy that is robust against model uncertainty as given by an additive uncertainty model and optimize the robust controller so as to optimize performance while remaining robustly monotonically convergent.
Proceedings ArticleDOI
A Two Step Optimization Based Iterative Learning Control Algorithm
TL;DR: This article introduces a general formulation of model based iterative learning control, valid for both linear and nonlinear systems, and conventional linear ILC is shown to be a particular case of this general formulation.
References
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Numerical recipes in C
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
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Optimization by Vector Space Methods
TL;DR: This book shows engineers how to use optimization theory to solve complex problems with a minimum of mathematics and unifies the large field of optimization with a few geometric principles.
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Optimal Control
TL;DR: Reading optimal control frank l lewis solution manual ebook pdf 2019 is extremely useful because you could get enough detailed information in the book technology has.