Recursive Subspace Least Squares Estimation for Data-Driven Model Predictive Control and Its Application to Aeroengines
Model predictive control (MPC) is subject to lim-itations, including stringent model accuracy requirements and time-consuming modeling, which impede its broader application. To address these challenges, this article proposes a predic-tive controller based on Data-Driven Recursive Subspace Least Squa...
Saved in:
Published in | IEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 7 |
---|---|
Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
18.08.2024
|
Subjects | |
Online Access | Get full text |
ISSN | 2378-363X |
DOI | 10.1109/INDIN58382.2024.10774346 |
Cover
Summary: | Model predictive control (MPC) is subject to lim-itations, including stringent model accuracy requirements and time-consuming modeling, which impede its broader application. To address these challenges, this article proposes a predic-tive controller based on Data-Driven Recursive Subspace Least Squares (DD-RSPC), obviating the need for any prior model identification. Unlike traditional MPC, DD-RSPC dynamically updates the prediction model using real-time measured input-output data, thereby significantly enhancing system adaptability. Furthermore, this article introduces a rigorous stability theorem that employs the Lyapunov function to ensure the stability of the DD-RSPC approach. Finally, the effectiveness of DD-RSPC is validated through aeroengine simulation experiments, effectively substantiating its adaptability, stability, and practical applicability. |
---|---|
ISSN: | 2378-363X |
DOI: | 10.1109/INDIN58382.2024.10774346 |