Optimization of Position Control of Induction Motors using Compact Genetic Algorithms

This paper describes a design procedure for a cascaded control system of induction motors based on compact genetic algorithms (cGAs). CGAs are search methods that 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 requirements and evenly distributed computational loads with respect to the standard, population-based GA. This paper investigates the applicability of a cGAs selected from literature to simultaneously optimize the couple of position and speed controllers using a weighted cost function that combines indices about position, speed, and current responses. The search is performed on-line, iteratively experimenting new solutions directly on the induction motor drive. The cascaded control system obtained through genetic search outperforms alternative schemes obtained with linear design techniques