Work

Phased Long Short-Term Memory-Based Predictive Control of Chemical Processes With Asynchronous and Delayed Measurements

Year: 2025

Type: article

Abstract: This work develops novel machine learning modeling and predictive control techniques for nonlinear chemical systems that experience asynchronous and delayed measurements that result in missingness in both offline and online data collections. Specifically, a phased long short-term memory (PLSTM) network is used to learn the process dynamics amidst the missingness in the data measurements, during the offline training process. The generalization performance of PLSTM is theoretically studied on the basis of statistical machine learning theory to better understand the capabilities of PLSTM models. The PLSTM model is subsequently employed to forecast the evolution of states for a Lyapunov-based model predictive control (LMPC). The proposed PLSTM-based LMPC is designed to account for data loss and delays in real-time implementation, and guarantees the closed-loop stability of nonlinear systems subjected to missing real-time data, provided that there is an upper bound on the number of consecutively missing real-time data. Finally, two chemical processes including an extractive dividing wall column (EDWC) and a continuous stirred tank reactor (CSTR) are used to demonstrate the effectiveness of PLSTM modeling and predictive control methods.

Source: IEEE Transactions on Control Systems Technology

DOI: 10.1109/tcst.2025.3587908

Authors: Wanlu Wu, Yujia Wang, Haohao Zhang, Ming-Qing Zhang, Min-Sen Chiu, Zhe Wu

Institutions: National University of Singapore, Beijing University of Chemical Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Ministry of Education