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Tensor Nuclear Norm LPV Subspace Identification
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DOI：10.1109/TAC.2018.2800772.png)
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摘要
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Linear parameter varying (LPV) subspace identification methods suffer from an exponential growth in number of parameters to estimate. This results in problems with ill-conditioning. In literature, attempts have been made to address the ill-conditioning by using regularization. Its effectiveness hinges on suitable a priori knowledge. In this paper. we propose using a novel, alternative regularization. That is, we first show that the LPV sub-Markov parameters can be organized into several tensors that are multilinear low rank by construction. Namely, their matricization along any mode is a low-rank matrix. Then, we propose a novel convex method with tensor nuclear norm regularization, which exploits this low-rank property. Simulation results show that the novel method can have higher performance than the regularized LPV-PBSIDopt technique in terms of variance accounted for.
Keyword:
Closed-loop identification
identification
linear parameter varying (LPV) systems
subspace methods
tensor regression
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IF:
7
论文数:
1.3W
被引数:
6.7W
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