A comparison of SPSA method and compact genetic algorithm for the optimization of induction motor position control

This paper describes the implementation of self-optimizing embedded control schemes for induction motor drives. The online design problem is formulated as a search problem and solved with stochastic optimization algorithms. The objective function takes into account the tracking error, and is directly measured on the hardware bench. In particular, we compare two efficient optimization algorithms, a simultaneous perturbation stochastic approximation method, and a compact genetic algorithm. Both search strategies have very small computational requirements, and therefore can be directly implemented on the same processor running the control algorithm.