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Subspace identification (SID) has been widely used in system identificationand control fields since it can estimate system models only relying on theinput and output data by reliable numerical operations such as singular valuedecomposition (SVD). However, high-dimension Hankel matrices are involved tostore these data and used to obtain the system models, which increases thecomputation amount of SID and leads SID not suitable for the large-scale orreal-time identification tasks. In this paper, a novel fast SID method based oncloud workflow processing and container technology is proposed to acceleratethe traditional algorithm. First, a workflow-based structure of SID is designedto match the distributed cloud environment, based on the computational featureof each calculation stage. Second, a containerised cloud workflow processingsystem is established to execute the logic- and data- dependent SID workflowmission based on Kubernetes system. Finally, the experiments show that thecomputation time is reduced by at most $91.6\%$ for large-scale SID mission anddecreased to within 20 ms for the real-time mission parameter.

Fast Subspace Identification Method Based on Containerised Cloud Workflow Processing System