- Title
- Constrained subspace method for the identification of structured state-space models (COSMOS)
- Creator
- Yu, Chengpu; Ljung, Lennart; Wills, Adrian; Verhaegen, Michel
- Relation
- IEEE Transactions on Automatic Control Vol. 65, Issue 10, p. 4201-4214
- Publisher Link
- http://dx.doi.org/10.1109/TAC.2019.2957703
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2020
- Description
- In this article, a unified identification framework called constrained subspace method for structured state-space models (COSMOS) is presented, where the structure is defined by a user-specified linear or polynomial parametrization. The new approach operates directly from the input and output data, which differs from the traditional two-step method that first obtains a state-space realization followed by the system-parameter estimation. The new identification framework relies on a subspace inspired linear regression problem which may not yield a consistent estimate in the presence of process noise. To alleviate this problem, the linear regression formulation is imposed by structured and low-rank constraints in terms of a finite set of system Markov parameters and the user specified model parameters. The nonconvex nature of the constrained optimization problem is dealt with by transforming the problem into a difference-of-convex optimization problem, which is then handled by the sequential convex programming strategy. Numerical simulation examples show that the proposed identification method is more robust than the classical prediction-error method initialized by random initial values in converging to local minima, but at the cost of heavier computational burden.
- Subject
- Hankel matrix factorization; Markov-parameter estimation; subspace identification; structured state-space models (COSMOS)
- Identifier
- http://hdl.handle.net/1959.13/1430969
- Identifier
- uon:38902
- Identifier
- ISSN:0018-9286
- Language
- eng
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