Abstract
Large-dimensional systems are ubiquitous in industrial manufacturing systems, smart grids, and biological systems. This article develops a reduced-dimensional latent state-space identification (LaSSID) framework for systems where the output dynamics is in a low-dimensional latent space. The proposed LaSSID algorithm effectively handles collinear dynamics among output variables and improves the condition number. The method is compared to traditional methods in terms of enhanced predictability and interpretability. In addition, a modified system identification experiment is developed using the Tennessee Eastman process simulation, incorporating reduced-dimensional excitations and collinear perturbations to simulate real-world scenarios. The results of this simulated experiment and the Dow Challenge industrial dataset demonstrate the superior performance of the proposed algorithm in terms of system identification accuracy and robustness to noise.
| Original language | English |
|---|---|
| Pages (from-to) | 7057-7065 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 21 |
| Issue number | 9 |
| Early online date | 18 Jun 2025 |
| DOIs | |
| Publication status | Published - Sept 2025 |
Bibliographical note
Publisher Copyright:© 2005-2012 IEEE.
Funding
This work was supported in part by a General Research Fund by the Research Grants Council (RGC) of Hong Kong SAR, China (Project No. 11303421); in part by a Collaborative Research Fund by RGC of Hong Kong (Project No. C1143-20 G); in part by a grant from ITF - Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project Ref. No. GHP/145/20); and in part by an InnoHK initiative of The Government of the HKSAR for the Laboratory for AI-Powered Financial Technologies. J. Yu would like to thank Dr. Y. Dong for the advice during his Ph.D. study.
Keywords
- Latent state-space model (SSM)
- process control
- reduced-dimensional dynamics
- reduced-dimensional system identification
