GA-Based Adaptive Fuzzy Logic Controller for Switched Reluctance Motor Drive

This paper develops a genetic algorithm (GA) based adaptive fuzzy logic controller (FLC) with four-parameter for the speed control of switched reluctance motor (SRM) drive. Different from the conventional control rules expressed by linguistic rules and inferred by Mamdani inference method, the control rules could also be expressed by a set of equations. The advantage of this method is that the complex Mamdani inference process can be avoided and the control rules can be tuned conveniently by adjusting the parameters. In different speed response stage, the parameter added to speed error and change-in-error is different. When the error is large, the parameter added to the error should be large in order to reduce error quickly. When the error is small, the parameter added to the error should be small and the parameter added to the error-in-change should be large in order to keep system steady and avoid overshoot. So a FLC with multi-parameter is required. In order to get a high performance, the parameters should be optimized. The conventional optimal method, such as gradient descent method, is easy to get stuck in local optimum. In this paper, GA is used to optimize the parameters of the proposed controller. GA is an optimization technique that performs a parallel, stochastic, but directed search to evolve the most fit population. It is a powerful tool to search optimum in complex optimal problem. Experimental results demonstrate that the proposed controller presents a high performance in terms of settling time, overshoot and response time.