NDLI: An approach to iterative learning control for spatio-temporal dynamics using nD discrete linear systems models

Content Provider	Springer Nature Link
Author	Cichy, Błażej Gałkowski, Krzysztof Rogers, Eric Kummert, Anton
Copyright Year	2010
Abstract	Iterative Learning Control (ILC) is now well established in terms of both the underlying theory and experimental application. This approach is specifically targeted at cases where the same operation is repeated over a finite duration with resetting between successive executions. Each execution is known as a trial and the key idea is to use information from previous trials to update the control input used on the current one with the aim of improving performance from trial-to-trial. In this paper, the subject area is the application of ILC to spatio-temporal systems described by a linear partial differential equation (PDE) using a discrete approximation of the dynamics, where there are a number of construction methods that could be applied. Here explicit discretization is used, resulting in a multidimensional, or nD, discrete linear system on which to base control law design, where n denotes the number of directions of information propagation and is equal to the total number of indeterminates in the PDE. The resulting control laws can be computed using Linear Matrix Inequalities (LMIs) and a numerical example is given. Finally, a natural extension to robust control is noted and areas for further research briefly discussed.
Starting Page	83
Ending Page	96
Page Count	14
File Format	PDF
ISSN	09236082
Journal	Multidimensional Systems and Signal Processing
Volume Number	22
Issue Number	1-3
e-ISSN	15730824
Language	English
Publisher	Springer US
Publisher Date	2010-05-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Iterative learning control nD linear systems PDEs Explicit discretization Artificial Intelligence (incl. Robotics) Signal, Image and Speech Processing Electrical Engineering Circuits and Systems
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Signal Processing Information Systems Computer Science Applications Software Hardware and Architecture

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