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Author
Date
2023-09-12Type
- Master Thesis
ETH Bibliography
yes
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Abstract
In this project, we extend the application of two typical closed-loop identification methods, namely the dual-Youla parameterization and the two-stage identification, from identifying LTI SISO systems to MIMO systems while considering the predefined sparsity structure in the system. Both extended methods result in a quadratic program that consists of 1) a quadratic cost function that minimizes the model fitting error, and 2) a group of linear equality constraints that ensure the estimated system has the desired sparsity structure. These two methods are applicable in distinct scenarios. In particular, the dual-Youla method requires the knowledge of the controller and guarantees the estimated plant is stabilized by the known controller, whereas the two-stage method does not rely on the controller’s knowledge. Both methods are proven to admit a consistent estimate of the plant theoretically. Finally, to showcase the practical efficacy of the proposed method, we apply our approach to identifying an inherently unstable and sparse irrigation networked system directly using closed-loop noisy data. Given the inherent instability of the irrigation network, we exclusively employ the extended dual-Youla method, as it guarantees stabilizability. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000647267Publication status
publishedPublisher
ETH ZurichOrganisational unit
08814 - Smith, Roy (Tit.-Prof.)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000684358
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ETH Bibliography
yes
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