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eBook Iterative Learning Control for Deterministic Systems (Advances in Industrial Control) ePub

eBook Iterative Learning Control for Deterministic Systems (Advances in Industrial Control) ePub

by Kevin L. Moore

  • ISBN: 0387197079
  • Category: Computer Science
  • Subcategory: Computers
  • Author: Kevin L. Moore
  • Language: English
  • Publisher: Springer Verlag (August 1, 1992)
  • Pages: 168
  • ePub book: 1462 kb
  • Fb2 book: 1522 kb
  • Other: mbr docx rtf lit
  • Rating: 4.3
  • Votes: 461

Description

Further chapters focus upon learning control for deterministic nonlinear systems . Iterative Learning Control: An Overview.

Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. The book concludes with the application of artificial neural networks to the learning control problem. Linear Time-Invariant Learning Control. LTI Learning Control via Parameter Estimation.

Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators

Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. Three specific ways to neural nets for this purpose are discussed, including two methods which use backpropagation training and reinforcement learning.

Автор: Kevin L. Moore Название: Iterative Learning Control for Deterministic Systems Издательство . This book focuses to a large extent on computation and implementation methods of deterministic performance measures, .

This book focuses to a large extent on computation and implementation methods of deterministic performance measures, . the steady-state, volumetric, dynamic and temporal flexibility indices, in various applications.

Further chapters focus unpon learning control for deterministic nonlinear systems, and a. .oceedings{, title {Iterative Learning Control for Deterministic Systems}, author {Kevin L. Moore and M. A. T. Johnson and Michael J. Grimble}, year {1992} }.

Further chapters focus unpon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. Kevin L. Moore, M. Johnson, Michael J. Grimble.

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Iterative Learning Control. ILC Summer School I organized in 2003. YangQuan Chen’s ILC page. Survey paper I wrote: "Iterative Learning Control: An Expository Overview," in Applied and Computational Controls, Signal Processing, and Circuits, vol. 1, no. 1, 1998.

Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty.

Iterative Learning Control (ILC) is a method of tracking control for systems that work in a repetitive mode. Examples of systems that operate in a repetitive manner include robot arm manipulators, chemical batch processes and reliability testing rigs. In each of these tasks the system is required to perform the same action over and over again with high precision. This action is represented by the objective of accurately tracking a chosen reference signal. on a finite time interval.

Learning control is an iterative approach to the problem of improving transient behavior for processes that are repetitive in nature. In this article, we present some results on iterative learning control

Learning control is an iterative approach to the problem of improving transient behavior for processes that are repetitive in nature. In this article, we present some results on iterative learning control. This analysis offers: (1) insight into the nature of the solution of the learning control problem by deriving sufficient convergence conditions; (2) an approach to learning control for linear systems based on parameter estimation; and (3) an analysis that shows that for finite-horizon problems it is possible to design a learning control algorithm that converges, with memory, in one step.