Adaptive state feedback speed controller for PMSM based on Artificial Bee Colony algorithm

Abstract

This article focuses on the application of nature-inspired optimization algorithm for adaptive speed control of permanent synchronous motor (PMSM) drive with variable parameters. In the proposed approach, a state feedback controller (SFC) is utilized for speed control of the PMSM, while on-line adaptation of its coefficients is made with the help of Artificial Bee Colony (ABC) algorithm. Since ABC is the first time applied for adaptation of SFC, its necessary modifications are depicted with details. In order to assure stability and robustness of the considered control scheme, a linear–quadratic optimization method is employed during adaptation. To ensure repeatable response of the plant regardless of parameter’s variation, a model reference adaptive system (MRAS) is used. The proposed approach is examined in simulation and experimental studies, including variable moment of inertia, non-measurable load torque and unmodelled friction. These confirm that adaptive SFC based on ABC noticeably improves control performance in comparison to a non-adaptive one.