Pareto Optimal Weighting Factor Design of Predictive Current Controller of a Six-Phase Induction Machine Based on Particle Swarm Optimization Algorithm

Finite-set model predictive control (FS-MPC) as current (PCC) is considered an exciting option for the stator of multiphase machines due to their flexibility and easy inclusion constraints. The weighting factors (WFs) PCC must be tuned variables interest, such machine losses $x-y$ currents, typically performed by trial-and-error procedure. Tuning methods based on artificial neural network (ANN) or coefficient variation were proposed three-phase inverter motor drive applications. However, extension this concept application not straightforward, only empirical procedures have been reported. In context, article proposes optimal method tune WF multiobjective particle swarm optimization (MOPSO) algorithm. A Pareto dominance used MOPSO find values PCC, comparing parameters root-mean-square error tracking currents. offers a systematic approach selection, with algorithm implementation direct over size search space speed convergence. Simulation experimental results in steady-state transient conditions are provided validate offline tuning procedure six-phase induction machine. improvements RMSE can more than 500% subspace, minor effect notation="LaTeX">$\alpha -\beta $ subspace. Finally, extended complex cost function, compared ANN approach.