An Application of Novel Nature-Inspired Optimization Algorithms to Auto-Tuning State Feedback Speed Controller for PMSM

This paper focuses on application of novel nature-inspired optimization algorithms to auto-tuning state feedback speed controller (SFC) for permanent magnet synchronous motor (PMSM). An artificial bee colony (ABC) algorithm and a flower pollination algorithm (FPA) are employed to obtain weighting matrices Q and R elements in linear quadratic regulator (LQR) optimization process. Information concerning principles of operation and influence of the control parameters on the both optimization algorithms are presented. Necessary modifications of ABC and FPA required for their successful application in a field of automatic control of electrical drive are depicted, including introduction of constraint-handling method that assures proper and safety operation of the motor and construction of performance indexes to provide the fulfillment of specified requirements. To the best of our knowledge, this is the first time that FPA is applied to auto-tuning process of SFC. On the basis of numerous tests, including simulation and experimental results, it is shown that the ABC is the most useful among the two optimization algorithms to obtain a better performance of the PMSM drive.