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Evolving Control Systems Engineering Design Solutions

Control engineering design problems can often be conveniently formulated as multiobjective optimisation problems and evolutionary computing algorithms have proven effective in their solution. Industrial application examples will illustrate this. However, evolutionary multiobjective optimisation has traditionally concentrated on problems comprising 2 or 3 objectives and control design problems can often comprise a relatively large number of objectives. Such problems pose new challenges for algorithm design, visualisation and implementation. Progressive articulation of design preferences can be used to assist in reducing the region of interest for the search and, thereby, simplify the problem. Parallel coordinates have proved a useful tool for visualising many objectives in a 2-D graph and the computational grid and wireless PDAs offer technological solutions to implementation difficulties arising in complex system design.

Type of Seminar:
Public Seminar
Prof. Peter Fleming
University of Sheffield, UK
Oct 28, 2005   17:15

ETH Zentrum, Gloriastrasse 35, Building ETZ, Room E6
Contact Person:

Prof. Guzzella
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Biographical Sketch:
Peter Fleming is Chair of Industrial Systems and Control in the Department of Automatic Control and Systems Engineering, University of Sheffield, UK and is also Director of the Rolls-Royce University Technology Centre for Control and Systems Engineering. His control and systems engineering research interests include multi-criteria decision-making, optimisation and scheduling, grid computing, software for control system design and implementation, and real-time control and instrumentation. These interests have led to the development of close links with a variety of industries in sectors such as aerospace, power generation, food processing, pharmaceuticals and manufacturing.