Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System

TL;DR

This paper introduces a fast subspace identification method leveraging cloud workflow processing and container technology to significantly reduce computation time for large-scale and real-time system identification tasks.

Contribution

It proposes a novel cloud-based, containerized workflow structure for SID, enabling efficient distributed processing and real-time application.

Findings

Computation time reduced by up to 91.6% for large-scale SID tasks.

Achieved real-time identification within 20 ms.

Demonstrated effectiveness in cloud environment with Kubernetes.

Abstract

Subspace identification (SID) has been widely used in system identification and control fields since it can estimate system models only relying on the input and output data by reliable numerical operations such as singular value decomposition (SVD). However, 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 leads SID not suitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing and container technology is proposed to accelerate the traditional algorithm. First, a workflow-based structure 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 Kubernetes system. Finally, the experiments show that the computation time is reduced by at most $91.6\%$ for large-scale SID mission and decreased to within 20 ms for the real-time mission parameter.