Model reduction of nonlinear systems subject to input disturbances
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2017-07-10
Print Publication Date2017-05
Permanent link to this recordhttp://hdl.handle.net/10754/625676
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AbstractThe method of convex optimization is used as a tool for model reduction of a class of nonlinear systems in the presence of disturbances. It is shown that under some conditions the nonlinear disturbed system can be approximated by a reduced order nonlinear system with similar disturbance-output properties to the original plant. The proposed model reduction strategy preserves the nonlinearity and the input disturbance nature of the model. It guarantees a sufficiently small error between the outputs of the original and the reduced-order systems, and also maintains the properties of input-to-state stability. The matrices of the reduced order system are given in terms of a set of linear matrix inequalities (LMIs). The paper concludes with a demonstration of the proposed approach on model reduction of a nonlinear electronic circuit with additive disturbances.
CitationN’Doye I, Laleg-Kirati T-M (2017) Model reduction of nonlinear systems subject to input disturbances. 2017 American Control Conference (ACC). Available: http://dx.doi.org/10.23919/ACC.2017.7963486.
SponsorsThe research reported herein is supported by the King Abdullah University of Science and Technology (KAUST). The authors would like to thank Prof. Salim Ibrir from King Fahd University of Petroleum and Minerals (KFUPM) for most fruitful discussions that substantially improved the paper.
Conference/Event name2017 American Control Conference, ACC 2017