Results 61 to 70 of about 106 (92)

Identifiability Analysis of the Input Excitation of two Mechanical Systems using the Lie-derivative and the Empirical Gramian Method

Input estimation indicates a class of algorithms to estimate the input excitation of a dynamical system based on output measurements and a model of the system. The feasibility of the estimation is conditioned on the identifiability of input. Noteworthy, identifiability entails that the response of the dynamical system is sensitive to a specific ...
Thing, M., Abbiati, G.; id_orcid 0000-0002-5048-8505   +1 more
openaire   +1 more source

Fly motion vision is tuned to maximize signal energy transfer between mechanical input and sensor output

Humbert JS, Krapp HG, Baeder JD, Badrya C, Dawson IL, Huang JV, Hyslop A, Jung YS, Leroy A, Lutkus C, Mortimer B, Nagesh I, Ruah C, Walker SM, Yang Y, Żbikowski RW, Taylor GK.   +16 more
europepmc   +1 more source

Empirical differential Gramians for nonlinear model reduction [PDF]

Automatica, 2021
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.
Yu Kawano, Jacquelien M A Scherpen
exaly   +4 more sources

On Empirical System Gramians [PDF]

Proceedings in Applied Mathematics and Mechanics, 2019
AbstractState‐space realizations of input‐output systems or control systems are a widely used class of models in engineering, physics, chemistry and biology. For the qualitative and quantitative classification of such systems, the system‐theoretic properties of reachability and observability are essential, which are encoded in so‐called system Gramian ...
Sara Gründel, Christian Himpe
exaly   +3 more sources

Model Order Reduction of Glucose-Insulin Homeostasis Using Empirical Gramians and Balanced Truncation

IFAC-PapersOnLine, 2017
Abstract In this contribution, a novel method for model order reduction is presented. Since biological systems are often highly nonlinear or having a high number of parameters, the concept of empirical Gramians is considered for controllability and observability analysis.
Christoph Ament
exaly   +3 more sources

An improved method for nonlinear model reduction using balancing of empirical gramians

Computers and Chemical Engineering, 2002
Abstract Nonlinear model predictive control has become increasingly popular in the chemical process industry. Highly accurate models can now be simulated with modern dynamic simulators combined with powerful optimization algorithms. However, computational requirements grow with the complexity of the models.
Juergen Hahn
exaly   +2 more sources

On the use of empirical gramians for controllability and observability analysis

Proceedings of the 2005, American Control Conference, 2005., 2005
This short paper illustrates the use of empirical gramians for controllability/observability analysis of nonlinear systems and compares the extracted information to results obtained from linear gramians and nonlinear observability matrices. It is shown that empirical gramians can more accurately represent controllability/observability of a nonlinear ...
Abhay K. Singh, Juergen Hahn
openaire   +1 more source

Fast Low‐Rank Empirical Cross Gramians

PAMM, 2017
AbstractThe cross Gramian matrix encodes the input‐output coherence of linear control systems and is used in projection‐based model reduction. The empirical cross Gramian is a data‐driven variant of the cross Gramian which also extends to nonlinear systems.
Himpe, C., Leibner, T., Rave, S., Saak, J.   +3 more
openaire   +2 more sources

Observability Analyses and Trajectory Planning for Tracking of an Underwater Robot using Empirical Gramians 1

IFAC Postprint Volumes IPPV / International Federation of Automatic Control, 2014
Abstract In marine robotics, estimation of the position and orientation of an underwater agent requires lots of research efforts. Especially the realization of robot teams has opened new horizons, allowing for relative navigation based on relative range measurements between the agents.
Naveena Crasta, Christoph Ament, Thomas Glotzbach   +2 more
exaly   +2 more sources

Nonlinear power system model reduction based on empirical gramians

2016 IEEE International Conference on Power System Technology (POWERCON), 2016
An effective nonlinear model reduction approach, empirical Gramians balanced reduction approach, is studied, in order to reduce the computation complexity in nonlinear power system model application. The realization procedure is: firstly, computing the empirical controllable and observable Gramians matrices, secondly, computing the balance ...
Hongshan Zhao, Zengqiang Mi
exaly   +2 more sources