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Multivariable system identifications with exogenous inputs are widely encountered in real-time industrial process control and system identification experiments. This paper presents a low-dimensional latent state space identification algorithm that handles collinear variables and builds latent dynamic state space models with input-output data. The proposed method aims to project the original variables to a lower-dimensional space for optimal predictability while preserving the causal dynamics of process systems. The optimization of this algorithm is achieved through iterative procedures, where the dimension reduction and latent state space identification are performed in a unified manner. The effectiveness of the proposed algorithm is illustrated in the oil fractionator in a shell control process, compared to the traditional subspace identification method.
|Title of host publication||2023 IEEE Conference on Control Technology and Applications, CCTA 2023|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - Sept 2023|
|Event||2023 IEEE Conference on Control Technology and Applications, CCTA 2023 - Bridgetown, Barbados|
Duration: 16 Aug 2023 → 18 Aug 2023
|Conference||2023 IEEE Conference on Control Technology and Applications, CCTA 2023|
|Period||16/08/23 → 18/08/23|
Bibliographical noteFunding Information:
2Institute of Data Science, Lingnan University, Hong Kong. Corresponding author (firstname.lastname@example.org). He acknowledges the financial support from a Natural Science Foundation of China Project (U20A20189), a General Research Fund by RGC of Hong Kong (No. 11303421), an ITF - Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project Ref. No. GHP/145/20), and a Shenzhen-Hong Kong-Macau Science and Technology Project Category C (9240086).
© 2023 IEEE.
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