Issues of algebra and optimality in Iterative Learning Control 

Author:  Hätönen, Jari^{1} 
Organizations: 
^{1}University of Oulu, Faculty of Technology, Department of Process and Environmental Engineering 
Format:  ebook 
Version:  published version 
Access:  open 
Online Access:  PDF Full Text (PDF, 1.6 MB) 
Persistent link:  http://urn.fi/urn:isbn:9514273516 
Language:  English 
Published: 
2004

Publish Date:  20040611 
Thesis type:  Doctoral Dissertation 
Defence Note:  Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in Raahensali (Auditorium L10), Linnanmaa, on June 11th, 2004, at 12 noon. 
Reviewer: 
Professor Heikki Koivo Professor Pertti Mäkilä 
Description: 
AbstractIn this thesis a set of new algorithms is introduced for Iterative Learning Control (ILC) and Repetitive Control (RC). Both areas of study are relatively new in control theory, and the common denominator for them is that they concentrate on controlling systems that include either reference signals or disturbances which are periodic. This provides opportunities for using past information or experience so that the control system learns the control action that results in good performance in terms of reference tracking or disturbance rejection. The first major contribution of the thesis is the algebraic analysis of ILC systems. This analysis shows that in the discretetime case ILC algorithm design can be considered as designing a multivariable controller for a multivariable static plant and the reference signal that has to be tracked is a multivariable step function. Furthermore, the algebraic analysis reveals that timevarying algorithms should be used instead of timeinvariant ones in order to guarantee monotonic convergence of the error in norm. However, from the algebraic analysis it is not clear how to select the free parameters of a given ILC algorithm. Hence in this thesis optimisation methods are used to automate this design phase. Special emphasis is placed on the so called NormOptimal Iterative Learning Control (NOILC) that was originally developed in (Amann:1996) as a new result it is shown that a convex modification of the existing predictive algorithm will result in a considerable improvement in convergence speed. Because the NOILC algorithm is computationally quite complex, a new set of ParameterOptimal ILC algorithms are derived that converge under certain assumptions on the original plant. Three of these new algorithms will result in monotonic convergence to zero tracking error for an arbitrary discretetime, linear, timeinvariant plant. This a very strong property that has been earlier reported for only a small number of ILC algorithms. In the RC case it is shown that an existing RC algorithm that has been widely analysed and used in the research literature is in fact highly unrobust if the algorithm is implemented using sampleddata processing. Consequently, in this thesis a new optimality based discretetime RC algorithm is derived, which converges to zero tracking error asymptotically for an arbitrary linear, timeinvariant discretetime plant under mild controllability and observability conditions. see all

Series: 
Acta Universitatis Ouluensis. C, Technica 
ISSNE:  17962226 
ISBN:  9514273516 
ISBN Print:  9514273508 
Issue:  205 
Subjects:  
Copyright information: 
© University of Oulu, 2004. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited. 