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oai:riuma.uma.es:10630/399782026-02-03T10:52:23Zcom_10630_2254col_10630_37953

Long-Prediction Horizon FCS-MPC for Multiphase Electric Drives With a Selective Control Action Promotion Carrillo Ríos, Juan González Prieto, Ignacio González Prieto, Ángel Durán-Martínez, Mario Javier Aciego Gallardo, Juan José Electrónica industrial Control automático https://openpolicyfinder.jisc.ac.uk/id/publication/3475 Model predictive control (MPC) is an interesting regulation solution in the field of multiphase electric drives. Its performance has been improved from different perspectives, but most works implement a single-step horizon approach. The complexity of using long-prediction horizons along with the higher complexity of multiphase systems has hindered the real-time implementation of multistep strategies. In fact, the only attempt of designing a multistep solution for multiphase drives uses a smart selection of the voltage vector, reducing the computational burden. Unfortunately, this strategy completely disregards the voltage output selected as the better candidate in the second step (i.e., in k+3), hence missing valuable information that can be inherited from one sampling period to the next one. This work suggests informing the MPC by creating a so-called hall of fame that stores the switching state determined as a suitable candidate in the previous sampling time. Such vector is promoted in the iterative process by adding a term in the cost function that is activated when the switching state under evaluation is within the hall of fame. Experimental results in a six-phase drive confirm the capability of the proposed scheme to simultaneously improve the current tracking and reduce the switching frequency. 2025-09-18T12:35:51Z 2025-09-18T12:35:51Z 2023-11-22 journal article J. Carrillo-Ríos, I. González-Prieto, Á. González-Prieto, M. J. Durán and J. J. Aciego, "Long-Prediction Horizon FCS-MPC for Multiphase Electric Drives With a Selective Control Action Promotion," in IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 9982-9993, Sept. 2024, doi: 10.1109/TIE.2023.3329230. https://hdl.handle.net/10630/39978 10.1109/TIE.2023.3329230 eng open access IEEE