Model Reduction by Moment Matching for Linear and Nonlinear Systems

By Alessandro Astolfi. IEEE Transactions on Automatic Control, 2010

377 citations recorded by the source corpus.

The model reduction problem for (single-input, single-output) linear and nonlinear systems is addressed using the notion of moment. A re-visitation of the linear theory allows to obtain novel results for linear systems and to develop a nonlinear enhancement of the notion of moment. This, in turn, is used to pose and solve the model reduction problem by moment matching for nonlinear systems, to develop a notion of frequency response for nonlinear systems, and to solve model reduction problems in the presence of constraints on the reduced model. Connections between the proposed results, projection methods, the covariance extension problem and interpolation theory are presented. Finally, the theory is illustrated by means of simple worked out examples and case studies.

Subfields: Control and Systems Engineering, Mechanical Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ed9c69710b883e541204f20bdc67106fdd93bf07

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In Control and Systems Engineering

In Mechanical Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Galerkin approximation with proper orthogonal decomposition : new error estimates and illustrative examples (2012, methodology): “Modal analysis (linear or non-linear normal modes), but also the moment matching method [2,7] and balanced truncation [11, 26] belong to this first family.”
POD-DEIM model order reduction for strain softening viscoplasticity (2017, methodology): “Many MOR techniques (Proper Orthogonal Decomposition-Galerkin (POD-G) [7], Balanced Truncation [8] and Moment Matching [9] to cite a few examples) have proven effective in reducing the computational cost of a linear model.”