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Empirical differential Gramians for nonlinear model reduction

Authors: Yu Kawano; Jacquelien M. A. Scherpen;

Empirical differential Gramians for nonlinear model reduction

Abstract

In this paper, we present an empirical balanced truncation method for nonlinear systems with linear time-invariant input vector field components. First, we define differential reachability and observability Gramians. They are matrix valued functions of the state trajectory (i.e. the initial state and input trajectory) of the original nonlinear system, and it is difficult to find them as functions of the initial state and input. The main result of this paper is to show that for a fixed state trajectory, it is possible to compute the values of these Gramians by using impulse and initial state responses of the variational system. Therefore, balanced truncation is doable along the fixed state trajectory without solving nonlinear partial differential equations, differently from conventional nonlinear balancing methods. We further develop an approximation method, which only requires trajectories of the original nonlinear systems. Our methods are demonstrated by an RL network along a trajectory.

Country
Netherlands
Keywords

Observability, Model reduction, balanced truncation, Balanced truncation, System structure simplification, Systems and Control (eess.SY), Proper orthogonal decomposition, proper orthogonal decomposition, Attainable sets, reachability, Optimization and Control (math.OC), Optimization and Control, Nonlinear systems, model reduction, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Nonlinear systems in control theory, nonlinear systems, Systems and Control

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
Green
bronze