22 March 1996 Design of optimal neurocontrollers for the separately excited dc motor using a hybrid genetic algorithm-neural network approach
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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.
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Paul B. Watta, Paul B. Watta, Mohamad H. Hassoun, Mohamad H. Hassoun, Jerome Meisel, Jerome Meisel, "Design of optimal neurocontrollers for the separately excited dc motor using a hybrid genetic algorithm-neural network approach", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235915; https://doi.org/10.1117/12.235915
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