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