Details of Grant 

EPSRC Reference:	GR/J82652/01
Title:	DEVELOPMENT OF A LEARNING AUTOMATA METHODOLGY IN THE CONTEXT OF VEGHICLE SUSPENSION CONTROL
Principal Investigator:	Gordon, Professor T
Other Investigators:	Wu, Professor H Rodgers, Mr E
Researcher Co-Investigators:
Project Partners:	Ford Motor Co
Department:	Aeronautical and Automotive Engineering
Organisation:	Loughborough University
Scheme:	Standard Research (Pre-FEC)
Starts:	29 August 1994	Ends:	28 August 1997	Value (£):	127,890
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:	Transport Systems and Vehicles
Related Grants:
Panel History:
Summary on Grant Application Form
Our initial theoretical and simulation studies have indicated that learning automata can be used on-line to successfully learn how to control a range of complex and unmodelled dynamic systems. The approach represents a radical departure from traditional design methods of dynamic control systems, which are commonly based on a framework of system modelling or simplified test results. To further develop and demonstrate the learning automata based design methodology requires a dedicated experimental program, some theoretical analysis will be required; although the major part of this will be carried out in a parallel study, funded by the Department of Transport Technology. The key feature of this proposal is the actual implementation of an intelligent computer-based learning system; it is to be carried out on a motor vehicle suspension control system, comprising semi-active suspension actuators (and hydraulic anti-roll torsion bars). The automata and control procedures will be implemented on a fast multi-purpose digital signal processor fitted to the vehicle. The full system presents a demanding challenge to the methodology. In the early stages of the project a very much simplified ride control problem will be attempted; later, it is hoped that learning will be demonstrated on the full vehicle system. More generally, the methodology resulting from this research will enable learning automata to be applied in the future to a wide range of computerised industrial systems.
Key Findings
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Organisation Website:	http://www.lboro.ac.uk