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Multi-input/multi-output switched-linear system identification from input–output data
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DOI：10.1016/j.sigpro.2025.110345.png)
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Abstract
En 中文
In this paper, we propose a scheme to identify discrete-time, multi-input/multi-output switched-linear systems (MIMO-SLSs) from input–output measurements. The key step is an observer-based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation converts the state-space (SS) identification problem into a MIMO-SARX identification problem by compressing infinite strings of system Markov parameters into finite strings of observer Markov parameters. We study switch and discrete-state (submodel) identifiability and derive persistence of excitation conditions for hybrid inputs to recover discrete-states. Switching sequence and discrete-states are estimated in the observer domain by solving a convex-sparse optimization problem followed by two different subspace algorithms. Local-mode clustering then reveals discrete-states. A detailed numerical example illustrates performance of the proposed scheme.
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