Data-Driven Identification of Dissipative Linear Models for Nonlinear Systems
We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input-output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb th...
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Published in | IEEE transactions on automatic control Vol. 67; no. 9; pp. 4978 - 4985 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input-output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of the linear model to enforce dissipativity, while closely approximating the dynamical behavior of the nonlinear system. Further, we provide an analytical relationship between the size of the perturbation and the radius in which the dissipativity of the linear model guarantees local dissipativity of the unknown nonlinear system. We demonstrate the application of this identification technique through two examples. |
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ISSN: | 0018-9286 1558-2523 |
DOI: | 10.1109/TAC.2022.3180810 |