Details of Grant 

EPSRC Reference:	GR/R74956/01
Title:	Iterative Learning and Repetitive Control with Application to Multi-axis Systems
Principal Investigator:	Owens, Professor DH
Other Investigators:
Researcher Co-Investigators:	Dr M French Professor P Lewin Professor E Rogers
Project Partners:
Department:	Automatic Control and Systems Eng
Organisation:	University of Sheffield
Scheme:	Standard Research (Pre-FEC)
Starts:	01 July 2002	Ends:	30 September 2005	Value (£):	201,220
EPSRC Research Topic Classifications:	Control Engineering Intelligent Measurement Sys.
EPSRC Industrial Sector Classifications:	Information Technologies
Related Grants:	GR/R74239/01
Panel History:
Summary on Grant Application Form
Iterative learning control (ILC) is a technique which can be used to control systems operating in a repetitive mode with the additional requirement that a specified output trajectory defined over a finite interval is followed to a high precision. It has applications across many areas (including robotics, process startup and manufacturing) and is closely related to a number of other design methodologies, principally repetitive control. This collaborative proposal has two major general objectives which aim firstly to both broaden the theoretical scope of ILC and, secondly, to supply essential benchmarking data for the performance of ILC schemes (both stand alone and comparatively). The latter aspect will be approached by the joint development and commissioning of an experimental facility (based on preliminary work by the PI of the Southampton co-application) in the form of a reconfigurable multiaxis conveyor system. The former aspect will aim to further develop the essentially 2D ILC theory initiated by Owens (and subsequently by Owens, Rogers, Amann and others) to produce both structural results and applicable design techniques. Where appropriate, results from the experimental part will be used to guide the theoretical investigations. Attention will be focussed on the development of techniques appropriate to practical situations of plant uncertainty or nonlinearity with particular attention given to the development of analysis and designs tools for model-based adaptive, optimal and predictive control paradigms.
Key Findings
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Further Information:
Organisation Website:	http://www.shef.ac.uk