Comparison of Constraint-handling Techniques Used in Artificial Bee Colony Algorithm for Auto-Tuning of State Feedback Speed Controller for PMSM

This article focuses on comparison of two constraint-handling techniques: Deb’s Rules (DR) and Augmented Lagrangian (AL) applied to Artificial Bee Colony (ABC) algorithm that is used for auto-tuning of state feedback speed controller (SFC) for permanent magnet synchronous motor (PMSM). The task of the optimization algorithm is to determine the elements of Q and R weighting matrices in linear quadratic regulator (LQR) optimization process. Chosen matrices guarantee the best performance according to given optimization criteria. Safety and proper operation of the motor requires the use of constraint-handling (C-H) technique. The ABC in its original version cannot handle the constrained optimization problems, therefore necessary modifications of considered optimization algorithm are depicted. Simulation and experimental results showed that AL technique allows to obtain a better convergence of ABC algorithm and a better performance of the PMSM drive than DR technique.

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