Weighting Factors Tuning by Direct Feedback in Predictive Control of Multiphase Motors
Manuel R. Arahal, Manuel G. Satué, Kumars Rouzbehi, Francisco Colodro
TL;DR
The paper tackles the challenge of tuning Weighting Factors in Predictive Stator Current Control (PSCC) for multi-phase drives by introducing a closed-loop, feedback-based WF adaptation that directly links performance indicators to the WF values. It implements two PI controllers to adjust $\lambda_{xy}$ and $\lambda_{sc}$, thereby steering the performance indices $\Gamma_2$ and $\Gamma_3$ toward user-defined references. Demonstrated on a real five-phase induction motor with a real-time lab setup, the method achieves fast adaptation (sub-second) and handles changes in performance targets without relying on large data sets or complex optimization. The work demonstrates the inherent trade-offs among $\Gamma_1$, $\Gamma_2$, and $\Gamma_3$, while maintaining low computational burden and practical applicability to other drive configurations.
Abstract
Predictive Stator Current Control (PSCC) has been proposed for control of multi-phase drives. The flexibility offered by the use of a Cost Function has been used to deal with the increased number of phases. However, tuning of the Weighting Factors constitutes a problem. Intensive trial and error tests are usual in this context. Existing on-line selection methods, on the other hand, require large amounts of data and/or complex optimization procedures. The proposal of this paper is a closed-loop scheme that links Weighting Factors to performance indicators. In this way, optimal Weighting Factors are determined for each operating point. Also, changes in reference values for performance indicators are easily tackled. Unlike previous methods, the proposal carries very little computational burden. A case study is developed for a five-phase induction motor and assessed with real experimentation on a laboratory set-up.