Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters

P. G. Ipoum-Ngome; Daniel L. Mon-Nzongo; Jinquan Tang; Jose Rodriguez; Jin Tao

doi:10.1109/PRECEDE57319.2023.10174307

Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters

P. G. Ipoum-Ngome^*, Daniel L. Mon-Nzongo, Jinquan Tang, Jose Rodriguez, Jin Tao

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper proposes an inherent and effective method for updating the database which is critical for the model-free predictive controller. In contrast to existing update methods which require complex analytical expressions, a unified analytical update model is proposed as a sum of the actual current and input control gradients. The effectiveness of the proposed controller is validated in a two-level voltage source inverter connected to the grid and an RLE load. The simulation results show that DDPC effectively cancels the stagnant mode and compared with MPC, DDPC provides better current performance at normal conditions and has higher robustness under parameter mismatches.

Original language	English
Title of host publication	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350396867
ISBN (Print)	9798350396867
DOIs	https://doi.org/10.1109/PRECEDE57319.2023.10174307
State	Published - 2023
Event	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 - Wuhan, China Duration: 2023 → 2023

Publication series

Name	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

Conference

Conference	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Country/Territory	China
City	Wuhan
Period	16/06/23 → 19/06/23

Bibliographical note

Funding Information:
ACKNOWLEDGMENT “P.G. Ipoum-Ngome acknowledges the support of PearlElectric Co., Ltd Postdoctoral Science Foundation under Grant 283436”. “J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208, and 1221293”.

Funding Information:
This work was funded by the Chinese National Natural Science Foundation under Grand 51977039 and the central government guiding local science and technology development projects under Grand 2021L3005.

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Control and Optimization
Modeling and Simulation
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Mechanical Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/PRECEDE57319.2023.10174307

Cite this

Ipoum-Ngome, P. G., Mon-Nzongo, D. L., Tang, J., Rodriguez, J., & Tao, J. (2023). Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRECEDE57319.2023.10174307

Ipoum-Ngome, P. G. ; Mon-Nzongo, Daniel L. ; Tang, Jinquan et al. / Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

@inproceedings{46189b4edfb64d03af54208731a03e2f,

title = "Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters",

abstract = "This paper proposes an inherent and effective method for updating the database which is critical for the model-free predictive controller. In contrast to existing update methods which require complex analytical expressions, a unified analytical update model is proposed as a sum of the actual current and input control gradients. The effectiveness of the proposed controller is validated in a two-level voltage source inverter connected to the grid and an RLE load. The simulation results show that DDPC effectively cancels the stagnant mode and compared with MPC, DDPC provides better current performance at normal conditions and has higher robustness under parameter mismatches.",

keywords = "Model predictive control (MPC), data-driven predictive controls (DDPC), model-free predictive control (MFPC)",

author = "Ipoum-Ngome, {P. G.} and Mon-Nzongo, {Daniel L.} and Jinquan Tang and Jose Rodriguez and Jin Tao",

note = "Funding Information: ACKNOWLEDGMENT “P.G. Ipoum-Ngome acknowledges the support of PearlElectric Co., Ltd Postdoctoral Science Foundation under Grant 283436”. “J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208, and 1221293”. Funding Information: This work was funded by the Chinese National Natural Science Foundation under Grand 51977039 and the central government guiding local science and technology development projects under Grand 2021L3005. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 ; Conference date: 16-06-2023 Through 19-06-2023",

year = "2023",

doi = "10.1109/PRECEDE57319.2023.10174307",

language = "English",

isbn = "9798350396867",

series = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",

}

Ipoum-Ngome, PG, Mon-Nzongo, DL, Tang, J, Rodriguez, J & Tao, J 2023, Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters. in 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Wuhan, China, 16/06/23. https://doi.org/10.1109/PRECEDE57319.2023.10174307

Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters. / Ipoum-Ngome, P. G.; Mon-Nzongo, Daniel L.; Tang, Jinquan et al.
2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters

AU - Ipoum-Ngome, P. G.

AU - Mon-Nzongo, Daniel L.

AU - Tang, Jinquan

AU - Rodriguez, Jose

AU - Tao, Jin

N1 - Funding Information: ACKNOWLEDGMENT “P.G. Ipoum-Ngome acknowledges the support of PearlElectric Co., Ltd Postdoctoral Science Foundation under Grant 283436”. “J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208, and 1221293”. Funding Information: This work was funded by the Chinese National Natural Science Foundation under Grand 51977039 and the central government guiding local science and technology development projects under Grand 2021L3005. Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - This paper proposes an inherent and effective method for updating the database which is critical for the model-free predictive controller. In contrast to existing update methods which require complex analytical expressions, a unified analytical update model is proposed as a sum of the actual current and input control gradients. The effectiveness of the proposed controller is validated in a two-level voltage source inverter connected to the grid and an RLE load. The simulation results show that DDPC effectively cancels the stagnant mode and compared with MPC, DDPC provides better current performance at normal conditions and has higher robustness under parameter mismatches.

AB - This paper proposes an inherent and effective method for updating the database which is critical for the model-free predictive controller. In contrast to existing update methods which require complex analytical expressions, a unified analytical update model is proposed as a sum of the actual current and input control gradients. The effectiveness of the proposed controller is validated in a two-level voltage source inverter connected to the grid and an RLE load. The simulation results show that DDPC effectively cancels the stagnant mode and compared with MPC, DDPC provides better current performance at normal conditions and has higher robustness under parameter mismatches.

KW - Model predictive control (MPC)

KW - data-driven predictive controls (DDPC)

KW - model-free predictive control (MFPC)

UR - http://www.scopus.com/inward/record.url?scp=85166267331&partnerID=8YFLogxK

U2 - 10.1109/PRECEDE57319.2023.10174307

DO - 10.1109/PRECEDE57319.2023.10174307

M3 - Conference contribution

AN - SCOPUS:85166267331

SN - 9798350396867

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 -

Ipoum-Ngome PG, Mon-Nzongo DL, Tang J, Rodriguez J, Tao J. Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). doi: 10.1109/PRECEDE57319.2023.10174307

Data-Driven Predictive Control with Inherent Update Method for Two-Level Voltage Source Inverters

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this