A comparative analysis of SPSA algorithms for induction motor adaptive control

This paper describes the implementation of a self-optimizing embedded control scheme for an induction motor drive. The online design problem is formulated as a search problem and solved with a stochastic optimization algorithm. The objective function takes in account the tracking error, and is directly measured on the hardware bench. The online optimization is performed with the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms, which offer a very effective tradeoff between simplicity of implementation, speed of convergence and quality of the final solutions. Among the known SPSA algorithms considered in this paper, we also propose a novel variant inspired to the concept of elitism frequently used in evolutionary computation. To assess the relative performances of the various algorithms, the paper carries out a comprehensive analysis of a control scheme for an induction motor drive subject to time-varying load disturbances.