Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System

Subspace identification (SID) has been widely used in system identification and control fields, since it can estimate system models while only relying on the input and output data using reliable numerical operations. However, the high-dimension Hankel matrices are involved to store these data and used to obtain the system models, which increases the computation amount of SID and makes SID unsuitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing approach and container technology is proposed to accelerate the traditional algorithm. First, a workflow establishment method of SID is designed to match the distributed cloud environment, based on the computational feature of each calculation stage. Second, a containerised cloud workflow processing system is established to execute the logic- and data- dependent SID workflow mission based on the Kubernetes system. Finally, the experiments show that the computation time is reduced by at most 91.6% for the large-scale SID mission and decreased to within 20 ms for the real-time mission parameter. Note to Practitioners—Subspace identification has became a widely used method in various fields, including power grids, chemical processing, data-driven control, and fault detection. However, as systems become larger and more complex, the computational challenges increase. To address this issue, this paper proposes a workflow-based method for subspace identification that can be executed in a cloud environment to accelerate the process. This note outlines the steps that practitioners can take to apply this method. The first step is to design a workflow structure as proposed method in this paper. This structure should be customized to fit the specific needs of the practitioner’s application. The second step is to build a containerized cloud workflow processing system that can execute the workflow. This system should be based on the Kubernetes system and designed to handle the specific computational requirements of the workflow. Practitioners who work in fields where computational efficiency is crucial for system identification operations can benefit from the proposed method. By following the steps outlined above, practitioners can streamline the process of subspace identification and achieve improvements in computational efficiency.