Share Email Print
cover

Proceedings Paper

Design of optimal neurocontrollers for the separately excited dc motor using a hybrid genetic algorithm/neural network approach
Author(s): Paul B. Watta; Mohamad H. Hassoun; Jerome Meisel
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, we develop an optimal controller for the separately excited dc-motor. The motivating application for this new controller is electric vehicle propulsion systems, although industrial and manufacturing applications as well as consumer products can also benefit from this approach. To achieve these optimal electric motor controllers, we propose a hybrid genetic algorithm/neural network design approach. In this case, the global optimization properties of the genetic algorithm are combined with the learning and generalization abilities of neural networks to produce a smooth controller which globally minimizes some specified cost or criterion function. Simulation results indicate that such optimal controllers can significantly improve motor efficiency. In particular, for the 4000 lb hybrid-electric vehicle constructed at Wayne State University, the optimal controller produced by our hybrid genetic algorithm/neural network approach can improve the efficiency of the motor by as much as 28.7% over conventional controllers.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235915
Show Author Affiliations
Paul B. Watta, Wayne State Univ. (United States)
Mohamad H. Hassoun, Wayne State Univ. (United States)
Jerome Meisel, Wayne State Univ. (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top