TY - JOUR
T1 - Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter
AU - Wu, Wenjie
AU - Qiu, Lin
AU - Liu, Xing
AU - Ma, Jien
AU - Rodriguez, Jose
AU - Fang, Youtong
N1 - Publisher Copyright:
© 1982-2012 IEEE.
PY - 2024/4/1
Y1 - 2024/4/1
N2 - In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations while inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.
AB - In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations while inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.
KW - Dynamic linearization (DL)
KW - finite control-set model predictive control (FCS-MPC)
KW - robustness
KW - voltage-source inverter (VSI)
UR - http://www.scopus.com/inward/record.url?scp=85162925709&partnerID=8YFLogxK
U2 - 10.1109/TIE.2023.3274861
DO - 10.1109/TIE.2023.3274861
M3 - Article
AN - SCOPUS:85162925709
SN - 0278-0046
VL - 71
SP - 3275
EP - 3284
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 4
ER -