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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Proceedings ArticleDOI
Sparse iterative learning control (SPILC): When to sample for resource-efficiency?
Tom Oomen,Cristian R. Rojas +1 more
TL;DR: The proposed method is an optimal framework for ILC that enforces sparsity and related structure on the command signal through a convex relaxation relying on ℓ1 regularization.
Book ChapterDOI
A Robust Iterative Learning Control Algorithm for Uncertain Power Systems
TL;DR: The ILC problem is converted to the design problem of the linear approximation parameters of the input function in a subspace spanned by a set of basis functions and results on positive invariant sets of linear discrete-time systems allow for the development of a robust ILC algorithm.
Book ChapterDOI
Norm Optimal Iterative Learning Control for Improved Trajectory Tracking of Servo Motor
TL;DR: In this paper, a norm optimal iterative learning control (NOILC) scheme combined with proportional velocity (PV) feedback control is proposed to improve the tracking performance by learning the system dynamics through the past tracking errors and the control effort.
Dissertation
Frequency Domain Based Analysis and Design of Norm-Optimal Iterative Learning Control
TL;DR: A frequency domain analysis with a multiplicity formulation of model uncertainty is developed in this work to quantify and visualize the allowable modeling errors and allows for a more complete evaluation of the robustness of NO-ILC.
Proceedings Article
A data-driven design of optimal ILC for nonlinear systems
TL;DR: In this article, a new data-driven optimal design framework of iterative learning control (ILC) for a class of general nonlinear systems is presented, which consists of a control input iterative updating law and a gradient matrix iterative estimate law based on two quadratic criterions.
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.
Book
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.
Book
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.