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Heuristic optimization strategies for automated experimental control design in variable speed drives

This paper explores the automated experimental control design for variable speed drives using heuristic optimization algorithms. Both the structures and parameters of suitable digital speed controllers are optimized experimentally directly on the drive and the technique is tested with different types of mechanical load profiles whose dynamics is generated using a programmable load emulator. Two biologically-inspired heuristic algorithms, Genetic Algorithms (GA) and Bacteria Foraging algorithms (BF) are evaluated for the purpose of the paper and their performance are compared and contrasted. Finally a new hybrid strategy, denominated Hybrid Bacteria Foraging (HBF), mixing some characteristics of the two, is finally proposed as a more appropriate solution for control optimization in electrical drives.