Genetic Algorithm for Parameters Optimization of ANN-based Speed Controller

This paper is devoted to the field of artificial intelligence for drive control. In previous works, we presented possible advantages from using an artificial neural network (ANN) for speed control in a DTC-SVM (direct torque controlled-space vector modulated) drive. Learning of the neural controller was set on-line. Starting from a random configuration of the speed controller, the network adapts its weights according to an error criterion. Although the use of such specialized controller allows potential adaptive and robust control skills, tuning of an ANN for online learning control is a long iterative procedure. Indeed, optimization of the neural controller induces determination of ten parameters acting critically on the control dynamics. However, using optimization algorithms, one can reduce efforts to reveal this set of parameters. Several optimization algorithms are based on description of biological evolutions. We call such algorithms evolutionary algorithms (EA). Genetic algorithm (GA) is a EA inspired by genetic processes leading human race toward optimal individuals capable of controlling their environment. This paper presents GA for optimization of ANN-based speed controller for induction motor drive.