RT Journal Article
T1 Memory-based Model Predictive Control for Parameter Detuning in Multiphase Electric Machines
A1 González Prieto, Ángel
A1 González Prieto, Ignacio
A1 Dordevic, Obrad
A1 Aciego Gallardo, Juan José
A1 Montenegro, Jorge
A1 Durán-Martínez, Mario Javier
A1 Khan, Mohammad
K1 Control automático
K1 Motores eléctricos
K1 Ingeniería eléctrica
AB Model predictive control (MPC) is a popular control technique to regulate multiphase electric drives (EDs). Despite the well-known advantages of MPC, it is sensitive to parameter detuning and lacks the capability to eliminate steady-state errors. The appearance of an offset between the reference and measured currents can significantly jeopardize the performance of the ED. This article suggests the use of a memory-based model predictive control (MB-MPC) that activates a compensation term when the parameter mismatch is detected. The suggested MB-MPC is universal for any multiphase machine if spatial harmonics are neglected since the proposed method does not consider any of the secondary x–y planes. Experimental results in two different rigs with six- and nine-phase induction motors prove this universality as well as its capability to eliminate current and speed offsets.
PB IEEE
YR 2023
FD 2023-10-30
LK https://hdl.handle.net/10630/39982
UL https://hdl.handle.net/10630/39982
LA eng
NO A. Gonzalez-Prieto et al., "Memory-Based Model Predictive Control for Parameter Detuning in Multiphase Electric Machines," in IEEE Transactions on Power Electronics, vol. 39, no. 2, pp. 2546-2557, Feb. 2024, doi: 10.1109/TPEL.2023.3328427
NO https://openpolicyfinder.jisc.ac.uk/id/publication/3543
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026