Elitist compact genetic algorithms for induction motor self-tuning control

Compact GAs (cGAs) are search methods that, instead of evolving a population of solutions, process a probability vector describing the distribution of a hypothetical population with update rules inspired to the typical selection and recombination operations of a GA. The cGAs well lend themselves to real-time implementations in constrained, low capacity microcontrollers, as they have reduced memory requirement and better distributed computational loads with respect to the standard, population-based GA. This paper investigates the applicability of two cGAs selected from literature to optimize online a PI controller for an induction motor drive. The experimental results are particularly promising, and suggest interesting directions for further research