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Award ID contains: 2141293

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  1. ; (, ASME Letters in Dynamic Systems and Control)
    Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback. 
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  2. ; ; (, IEEE)
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  3. ; ; (, IEEE)
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  4. ; (, Proceedings of 2023 American Control Conference)
    This paper presents a least squares formulation and a closed-form solution for identifying dynamical systems using irregular and sparse data obtained by chronologically merging measurements taken by multiple slow sensors of different sampling rates. We provide the theoretical foundation for developing advanced least-squares-based system identification algorithms for cases where the input-output data are asynchronous and/or scarce. Demonstrative examples are provided to validate the proposed method, and indicate the potential of removing the Nyquist sampling limitation in system identification. We provide in details how using 19 percent of the full measurements enables to capture the dynamics of a dynamic system when two slow sensors are collaboratively collecting the system response at different speeds. The required measurements can be further reduced under the proposed collaborative sensing scheme. 
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