Abstract
This paper considers a general approach for the identification of partial differential equation-governed spatially-distributed systems. Spatial discretization virtually divides a system into spatially-interconnected subsystems, which allows to define the identification problem at the subsystem level. Here we focus on such a distributed identification of spatially-interconnected systems with temporal/spatial varying properties, whose dynamics can be captured by temporal/spatial linear parameter-varying (LPV) models. Inaccurate selection of the functional dependencies of the model parameters on scheduling variables may lead to bias in the identified models. Hence, we propose a non-parametric identification approach via a least-squares support vector machine (LS-SVM) - `non-parametric' estimation is in the sense that the model dependence on the scheduling variables is not explicitly parametrized. The performance of the proposed approach is evaluated on an Euler-Bernoulli beam with varying thickness.
Original language | English |
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Title of host publication | Proceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts |
Publisher | American Automatic Control Council (AACC) |
Pages | 4592-4597 |
ISBN (Print) | 978-1-4673-8682-1 |
DOIs | |
Publication status | Published - 2016 |
Event | 2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA - Boston Marriott Copley Place, Boston, MA, United States Duration: 6 Jul 2016 → 8 Jul 2016 http://acc2016.a2c2.org/ |
Conference
Conference | 2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA |
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Abbreviated title | ACC 2016 |
Country/Territory | United States |
City | Boston, MA |
Period | 6/07/16 → 8/07/16 |
Internet address |