Neuro-Genetic Adaptive Optimal Controller for DC Motor

Abstract

Conventional speed controllers of DC motors suffer from being not adaptive, this is because of the nonlinearity in the motor model due to saturation. Structure of DC motor speed controller should vary according to its operating conditions, so that the transient performance is acceptable. In this paper an adaptive and optimal Neuro-Genetic controller is used to control a DC motor speed. GA will be used first to obtain the optimal controller parameter for each load torque and motor reference speed. The data obtained from GA is used to train a neural network; the inputs for the neural network are the load torque and the motor reference speed and the outputs are the controller parameters. This neural network is used on line to adapt the controller parameters according to operating conditions. This controller is tested with a sudden change in the operating conditions and could adapt itself for the conditions and gave an optimal transient performance.

DOI: http://dx.doi.org/10.11591/ijpeds.v4i3.5750