TY - GEN
T1 - Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm
AU - Heydari, Rasool
AU - Young, Hector
AU - Rafiee, Zahra
AU - Flores-Bahamonde, Freddy
AU - Savaghebi, Mehdi
AU - Rodriguez, Jose
N1 - Funding Information:
The authors want to acknowledge the support of ANID through projects FB0008, ACT192013 and 1170167.
Funding Information:
Also, acknowledge the support of SERC Chile (CONI-CYT/FONDAP/15110019).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10/18
Y1 - 2020/10/18
N2 - The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.
AB - The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.
KW - auto-regressive with exogenous input (ARX)
KW - model identification
KW - model predictive control (MPC)
KW - Model-free predictive control
KW - robustness
KW - voltage source inverter
UR - http://www.scopus.com/inward/record.url?scp=85097743842&partnerID=8YFLogxK
U2 - 10.1109/IECON43393.2020.9254834
DO - 10.1109/IECON43393.2020.9254834
M3 - Conference contribution
AN - SCOPUS:85097743842
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 3065
EP - 3070
BT - Proceedings - IECON 2020
PB - IEEE Computer Society
T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Y2 - 19 October 2020 through 21 October 2020
ER -