TY - GEN
T1 - Low Updating Frequency Model-Free Predictive Control Using Time-Series Model on PMSM Drives
AU - Wei, Yao
AU - Ke, Dongliang
AU - Zuo, Kunkun
AU - Wang, Fengxiang
AU - Kennel, Ralph
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The model-free predictive control (MFPC) strategy is commonly used to address the problem of weak robustness in model-based predictive control (MBPC). However, due to the estimation algorithm requiring large processor resources in this strategy, the possibility of overrun errors cannot be avoided fully. To release this problem, a low updating frequency MFPC is presented in this paper to prevent overrun error and reduce the influences on model accuracy. The algorithm is based on a time-series model with a recursive least square (RLS) estimation algorithm, and uses spline interpolation to obtain multiple groups of extended data and insert them into the regressive vector. The latest terms of the vector are used to generate coefficients in the model and predict the output signals to the next sampling period through multi-step prediction. The error and stability are analyzed theoretically, and the advantages of this method, including better model quality and current quality with enhanced robustness, are verified through experimental results.
AB - The model-free predictive control (MFPC) strategy is commonly used to address the problem of weak robustness in model-based predictive control (MBPC). However, due to the estimation algorithm requiring large processor resources in this strategy, the possibility of overrun errors cannot be avoided fully. To release this problem, a low updating frequency MFPC is presented in this paper to prevent overrun error and reduce the influences on model accuracy. The algorithm is based on a time-series model with a recursive least square (RLS) estimation algorithm, and uses spline interpolation to obtain multiple groups of extended data and insert them into the regressive vector. The latest terms of the vector are used to generate coefficients in the model and predict the output signals to the next sampling period through multi-step prediction. The error and stability are analyzed theoretically, and the advantages of this method, including better model quality and current quality with enhanced robustness, are verified through experimental results.
KW - Low Updating Frequency
KW - Model Accuracy
KW - Model-Free Predictive Control
KW - Regressive Vector
KW - Spline Interpolation
UR - http://www.scopus.com/inward/record.url?scp=85198754222&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3940-0_15
DO - 10.1007/978-981-97-3940-0_15
M3 - Conference contribution
AN - SCOPUS:85198754222
SN - 9789819739394
T3 - Lecture Notes in Electrical Engineering
SP - 138
EP - 151
BT - Conference Proceedings of the 2023 3rd International Joint Conference on Energy, Electrical and Power Engineering
A2 - Hu, Cungang
A2 - Cao, Wenping
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Joint Conference on Energy, Electrical and Power Engineering, CoEEPE 2023
Y2 - 22 November 2023 through 24 November 2023
ER -