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A novel state-space model identification method from a behavioral system-theoretic perspective
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DOI：10.1016/j.jprocont.2026.103732.png)
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Abstract
En 中文
As the mainstream methodology for identifying state-space models, subspace identification relies on the orthogonality assumption between data spaces, and could thus lead to unsatisfactory identification accuracy in finite-sample regime. To overcome this limitation, a novel state-space model identification method is proposed by leveraging the capability of behavioral systems theory in characterizing system dynamics with finite-length data trajectory. In virtue of the innovation-based data-driven output predictor (DDOP), a recent advance from this theoretical framework, the state-space model identification is converted into an innovation estimation problem followed by a model reduction step. To achieve better identification accuracy, an improved innovation estimation strategy incorporating low-rank prior is further proposed, formulated as a rank-constrained programming (RCP) problem and solved via the alternating direction method of multipliers (ADMM). Numerical and industrial dataset experiments demonstrate the superior modeling accuracy of the proposed method over existing subspace identification methods in both open-loop and closed-loop cases, with out-of-sample prediction error reduced by more than 23% on industrial benchmark datasets.
Keywords:
state-space model identification
behavioral systems theory
innovation estimation
rank-constrained programming
subspace identification
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