When Process Control Meets Big Data: Data-Driven Cloud-Edge Collaborative Predictive Control Method for Multiple Operating Conditions Processes

Abstract

Complex industrial processes often run under varying operating conditions. Learning-based control methods are difficult to adapt to these unknown variations. Therefore, it is necessary to update the model and control strategy adaptively. However, in traditional control frameworks, due to the limitation of computational and storage resources of edge devices, control strategies are difficult to update once deployed, which leads to model mismatch after operating condition change and seriously reduces the control performance. To solve this problem, this article proposes a novel cloud-edge collaborative control method. Specifically, a cloud-assisted parallel subspace identification method is proposed, which fully utilizes the powerful computational capability of the distributed cluster in the cloud to achieve fast and accurate model identification. Then, an explicit control strategy is proposed, which solves the control law as a piece-wise affine function offline. The process model and explicit control law are sent down to the edge, enabling fast and precise control under limited resource constraints. An operating condition change detection method based on the process model is proposed, and the edge detects the emergence of new operating conditions by the prediction error. Meanwhile, to fully excite new operating condition characteristics, a joint control and excitation signal generator (JCESG) is designed. JCESG ensures accurate identification of new operating condition model under limited data, which in turn greatly shortens the operation condition switching process and ensures fast modeling and precise control in new operating conditions. Notably, considering that the proposed method can adaptively realize model identification and control law update, it is capable of adapting to the continuous change of operating conditions, and the sufficient excitation of JCESG greatly reduces the data volume requirement for model update, which further ensures that the method adapts to the full range of operating conditions. Finally, extensive experiments verified the superiority of the proposed method.