Abstract: This paper presents a vision for the future design of electrical machines and the systems in which they are employed with a view to achieving a contribution to the overall energy consumption minimisation throughout industry. For example, developments in rare earth magnetic materials have enabled new designs of high power density, high efficiency machines. Computationally demanding design techniques such as finite elements and genetic algorithms are becoming practicable with advances in software and digital processors. This is enabling the progress of more sophisticated machine designs with special rotor and stator geometries yielding optimal flux paths, high torque and minimal ripple outputs. To achieve the aforementioned energy consumption minimisation, not only is the electrical machine design important but also the consideration of the energy losses in the systems employing the electrical machines. Automatic control will play an increasingly important role in this regard. Optimal control strategies, especially those involving nonlinearities, are of an open loop structure and hitherto have been largely of academic interest in view of their sensitivities to parametric errors and external disturbances. Advances in easily attained computational power, however, are enabling practicable closed loop versions of these optimal controls that overcome these limitations to be created, with the aid of artificial intelligence. This paper includes several applications in which combined electrical machine design for maximum efficiency and system design for minimum energy usage is of paramount importance.
 

Brief Biography of the Speakers:

Roy Perryman: graduated with a BSc(Hons) in Electrical Engineering in 1969 and gained a PhD in Magnetic Materials in 1974. He spent 17 years in the electrical and electronics industry working with AFA Minerva (EMI) Ltd, Bowthorpe Controls, and Walter Jones & Co Ltd. In 1988 he joined the University of Greenwich and became Associate Head of the School of Engineering. He was subsequently appointed as Head of the School of Electrical & Manufacturing Engineering at the University of East London in 1996 and became Ford Professor in Engineering Education in 2004. He is a Chartered Engineer and Fellow of the Institution of Engineering and Technology (FIET). His research interests are in the design and control of electrical machines and drive systems, magnetic materials, condition monitoring and the application of neural networks.

Stephen Dodds: received a BSc (Hons) in Electrical Engineering in 1967, an MSc in Systems Engineering in 1970 and a PhD in the Control of Flexible Spacecraft in 1985. He spent 13 years as an attitude and orbit control systems engineer on European space programmes and originated new digitally implemented spacecraft attitude control. In 1985 he was appointed Reader in Control Engineering at the University of East London (UEL) and subsequently expanded his control systems research to encompass electrical drives. In 1997 he was made an Academician of the Academy of Non-linear Sciences of Russia and became Professor of Control Engineering at UEL. His general research interests encompass robust control techniques and feedback linearisation, which has resulted in the recent innovations in drive control systems falling under the general heading of 'forced dynamic control'.