Hybrid Bacterial Foraging Optimization Strategy for Automated Experimental Control Design in Electrical Drives

This paper explores the automated experimental control design for variable speed drives using a novel heuristic optimization algorithm. A hybrid approach, which combines desirable characteristics of two of the most widely used biologically-inspired heuristic algorithms, the genetic algorithms (GAs) and the bacterial foraging (BF) algorithms, is studied and developed in this paper. Both the structures and parameters of digital speed controllers are optimized experimentally and directly on the drive while it is subject to different types of mechanical load; the dynamics of these load profiles are generated using a programmable load emulator. The proposed hybrid bacterial foraging (HBF) algorithm is evaluated, for the purpose of control optimization for electric drives, against GA and BF, and their performances are compared and contrasted.