Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives
Abstract In this paper, a innovative methodology for Switched Reluctance Motor (SRM) drive control using Smart Bacterial Foraging Algorithm (SBFA) is presented. This method mimics the chemotactic behavior of the E. Coli bacteria for optimization. The proposed algorithm uses individual and social intelligences, so that it can search responses among local optimums of the problem adaptively. This method is used to tune the coefficients of a conventional Proportion Integration (PI) speed controller for SRM drives with consideration of torque ripple reduction. This matter is done by applying the proposed algorithm to a multi objective function including both speed error and torque ripple. This drive is implemented using a DSP based (TMS320F2812) for an 8/6, 4 kW SRM. The simulation and experimental results confirm the improved performance of adjusted PI controller using SBFA in comparison with adjusted PI controller using standard BFA. Excellent dynamic performance, reduced torque ripple and current oscillation can be achieved when the coefficients of PI controller are optimized by using SBFA.