TY - GEN
T1 - Extended Prediction Horizon Multi-objective FSMPC without Weighting Factors
AU - Xie, Haotian
AU - Kennel, Ralph
AU - Wang, Fengxiang
AU - Heldwein, Marcelo Lobo
AU - Rodriguez, Jose
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Extended prediction horizon FS-MPC shows its merits of a low current harmonic distortion per switching frequency. However, the weighting factors are hard to be optimized in an extended prediction horizon FS-MPC with more involved control objectives. A multi-objective FS-MPC with a longer prediction horizon without the involvement of any weighting factors is proposed in this paper. The descriptions of control plant and FS-MPC with a prediction horizon of 3 are presented. The control objectives, torque, flux and switching frequency are optimized in the cost function. Therefore, there are two weighting factors to be optimized. To cope with the abovementioned issue, a ranking evaluation approach is proposed for weighting factor optimization in the proposed FSMPC method with an extended horizon to obtain two possible vectors, which are optimized in the next prediction horizon. The vector with a minimal ranking value is selected as the optimal. The simulation results of the proposed method on a pair of 2. 2kW induction machine drives are evaluated and compared.
AB - Extended prediction horizon FS-MPC shows its merits of a low current harmonic distortion per switching frequency. However, the weighting factors are hard to be optimized in an extended prediction horizon FS-MPC with more involved control objectives. A multi-objective FS-MPC with a longer prediction horizon without the involvement of any weighting factors is proposed in this paper. The descriptions of control plant and FS-MPC with a prediction horizon of 3 are presented. The control objectives, torque, flux and switching frequency are optimized in the cost function. Therefore, there are two weighting factors to be optimized. To cope with the abovementioned issue, a ranking evaluation approach is proposed for weighting factor optimization in the proposed FSMPC method with an extended horizon to obtain two possible vectors, which are optimized in the next prediction horizon. The vector with a minimal ranking value is selected as the optimal. The simulation results of the proposed method on a pair of 2. 2kW induction machine drives are evaluated and compared.
KW - extended prediction horizon
KW - model predictive control
KW - multi-objective
KW - weighting factor optimization
UR - http://www.scopus.com/inward/record.url?scp=85166239149&partnerID=8YFLogxK
U2 - 10.1109/PRECEDE57319.2023.10174576
DO - 10.1109/PRECEDE57319.2023.10174576
M3 - Conference contribution
AN - SCOPUS:85166239149
T3 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
BT - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Y2 - 16 June 2023 through 19 June 2023
ER -