AH
A Hansson
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1
This note considers the identification of large-scale 1D networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and a two-layer Toeplitz structural property in the data equation, while the state-space realization of a single subsystem is formulated as a structured low-rank matrix factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.
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This note considers the identification of large-scale 1D networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and a two-layer Toeplitz structural property in the data equation, while the state-space realization of a single subsystem is formulated as a structured low-rank matrix factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.
Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.
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Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.
N2SID
Nuclear norm subspace identification of innovation models
The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimization approach allows to include constraints on the unknown matrices in the data-equation characterizing subspace identification methods, such as the lower triangular block-Toeplitz of weighting matrices constructed from the Markov parameters of the unknown observer. The classical use of instrumental variables to remove the influence of the innovation term on the data equation in subspace identification is avoided. The avoidance of the instrumental variable projection step has the potential to improve the accuracy of the estimated model predictions, especially for short data length sequences
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The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimization approach allows to include constraints on the unknown matrices in the data-equation characterizing subspace identification methods, such as the lower triangular block-Toeplitz of weighting matrices constructed from the Markov parameters of the unknown observer. The classical use of instrumental variables to remove the influence of the innovation term on the data equation in subspace identification is avoided. The avoidance of the instrumental variable projection step has the potential to improve the accuracy of the estimated model predictions, especially for short data length sequences
This paper studies the local subspace identification of 1D homogeneous networked systems. The main challenge lies at the unmeasurable interconnection signals between neighboring subsystems. Since there are many unknown inputs to the concerned local system, the corresponding identification problem is semi-blind. To cope with this problem, a nuclear norm optimization based subspace identification is presented, which is carried out for solving the Markov parameters of a locally lifted system, followed by determining the system matrices of a single subsystem. In the step of Markov parameter estimation, we form a nuclear norm regularized optimization problem which can well handle the adverse effects of the unknown system inputs as long as the number of unknown system inputs is relatively small. In the step of system realization, we again derive a nuclear norm regularized optimization formulation which can cope with the under-determinedness of the realization problem. In the end, the overall identification algorithm is summarized.
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This paper studies the local subspace identification of 1D homogeneous networked systems. The main challenge lies at the unmeasurable interconnection signals between neighboring subsystems. Since there are many unknown inputs to the concerned local system, the corresponding identification problem is semi-blind. To cope with this problem, a nuclear norm optimization based subspace identification is presented, which is carried out for solving the Markov parameters of a locally lifted system, followed by determining the system matrices of a single subsystem. In the step of Markov parameter estimation, we form a nuclear norm regularized optimization problem which can well handle the adverse effects of the unknown system inputs as long as the number of unknown system inputs is relatively small. In the step of system realization, we again derive a nuclear norm regularized optimization formulation which can cope with the under-determinedness of the realization problem. In the end, the overall identification algorithm is summarized.