A Joint Spatial-Temporal Predictive Control Method for Multimode Distributed Parameter System With Unobserved States

The application of autonomous systems plays a crucial role in ensuring the safe and stable operation of industrial processes. Control methods grounded in artificial intelligence (AI) present new prospects for this application. However, the implementation of AI-based control methods typically depends on complete observation data. When unobservable states exist, it becomes challenging to achieve autonomous control. On the other hand, the process generally operates under dynamic working conditions, and the model needs to be updated in time to avoid model mismatch, which further increases the difficulty of autonomous control. To address these challenges, an autonomous control method based on a joint spatial-temporal model (AC-JSTM) is proposed to achieve autonomous control of unobservable point states of industrial processes under dynamic working conditions. Specifically, to solve the problem of model mismatch and poor control effect caused by the dynamic conditions of industrial processes, a condition identifier based on orthogonal test design (CI-OTD) is first proposed. The data set is constructed through typical condition parameter design, and the conditions are distinguished based on the correspondence between data distribution and condition labels. Then, considering the spatial distribution characteristics between unobservable and observable points and the dynamic correlation of observable points in the time dimension, a JSTM is established. The features of spatial and dynamic dimensions are integrated by a joint training method to achieve the prediction of unobservable points based on observable points. Finally, combining CI-OTD and JSTM, a dynamic working condition control (DWCC) framework is established to achieve autonomous control of unobservable points under dynamic working conditions. To verify the superiority and effectiveness of the proposed method, control experiments are designed for both catalytic rods and tubular reactors. The results show that the proposed method can achieve accurate control of unobservable points under dynamic conditions, and the control accuracy is improved by 18.69% compared with the control framework based on the spatial-temporal model.