Projection frameworks for model reduction of weakly nonlinear systems
Proceedings of the 37th Annual Design Automation Conference
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
NORM: compact model order reduction of weakly nonlinear systems
Proceedings of the 40th annual Design Automation Conference
Piecewise polynomial nonlinear model reduction
Proceedings of the 40th annual Design Automation Conference
Proceedings of the 40th annual Design Automation Conference
Analog Macromodeling using Kernel Methods
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Parameterized model order reduction of nonlinear dynamical systems
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Projection-based approaches for model reduction of weakly nonlinear, time-varying systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Typical nonlinear model order reduction approaches need to address two issues: reducing the order of the model, and approximating the vector field. In this paper we focus exclusively on the second issue, and present results characterizing the repercussions at the system level of vector field approximations. The error assessment problem is formulated as the L2 gain upper bounding problem of a scaled feedback interconnection. Applying the small gain theorem in the proposed setup, we prove that the L2 gain of the error system is upper bounded by the L2 gain of the vector field approximation error, provided it is small. In addition, the paper also presents a numerical procedure, based on the IQC/LMI approach, to perform the error estimation task with less conservatism. A numerical example is given in this paper to demonstrate the practical implications of the presented results.