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Comparing Dynamic System Models with Additive Uncertainty.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Comparing Dynamic System Models with Additive Uncertainty./
Author:	Karumanchi, Aditya.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	132 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30164337
ISBN:	9798352637050

Comparing Dynamic System Models with Additive Uncertainty.
Karumanchi, Aditya.

Comparing Dynamic System Models with Additive Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 132 p.

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2022.

This item must not be sold to any third party vendors.

Due to the complexity of the operational design domain of Automated Driving Systems, the industry is trending towards the use of simulation-based methods for their verification and validation (V\\&V), which rely on the use of models of the vehicles, sensors, vehicle environments, etc. Depending on the testing requirements, computational capabilities, modeling effort, and other such factors, these models can vary in fidelity. However, this variation in fidelity has an effect on the excitation of the control systems under test, and can therefore affect the results of the tests themselves. Moreover, since every model is an approximation of the actual physical system it represents, there is uncertainty associated with its output. Therefore, we need to be able to compare uncertain system models in order to understand the effect of model fidelity variation on test accuracy.The existing metrics such as Hankel Singular Values compare asymptotic behavior of the models, whereas simulation studies are over finite time. Although some of these metrics may be applied over finite time, they rely on hyper-parameters like weights on time or frequency.In this study, we propose an approach for computing a (pseudo)metric based on the literature for comparing the predictive performance of two models, called finite-time Kullback-Leibler (KL) rate. For any two general state space models with a general additive uncertainty, we first discuss an approach for propagating a general additive uncertainty (represented as a Gaussian Mixture Model (GMM) ) through a linear time-invariant system. We then apply this propagation approach to linear representations of nonlinear systems obtained through Dynamic Mode Decomposition (DMD). We illustrate this combined approach for the comparison of two lateral vehicle dynamics models over an obstacle avoidance maneuver to measure the effect of fidelity on the predictive performance of each model. We also apply this to a V&V problem, wherein we compare the application of the two vehicle dynamics models to reference vehicle trajectories. We see that the KL rate depends not only on the differences in the asymptotic spectral properties of each model, but also on the trajectory being simulated.Through these methods, we illustrate a process for comparing models of different fidelity over relevant trajectories. This enables us to understand which model is most appropriate for a particular scenario in a virtual test. We also show that the variation in the comparison metric can be connected to the phase space of the system. This enables us to use thresholds on the metric to decide when to switch between models of different fidelity.

ISBN: 9798352637050Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Dynamic systems

Comparing Dynamic System Models with Additive Uncertainty.
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300 $a 132 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
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520 $a Due to the complexity of the operational design domain of Automated Driving Systems, the industry is trending towards the use of simulation-based methods for their verification and validation (V\\&V), which rely on the use of models of the vehicles, sensors, vehicle environments, etc. Depending on the testing requirements, computational capabilities, modeling effort, and other such factors, these models can vary in fidelity. However, this variation in fidelity has an effect on the excitation of the control systems under test, and can therefore affect the results of the tests themselves. Moreover, since every model is an approximation of the actual physical system it represents, there is uncertainty associated with its output. Therefore, we need to be able to compare uncertain system models in order to understand the effect of model fidelity variation on test accuracy.The existing metrics such as Hankel Singular Values compare asymptotic behavior of the models, whereas simulation studies are over finite time. Although some of these metrics may be applied over finite time, they rely on hyper-parameters like weights on time or frequency.In this study, we propose an approach for computing a (pseudo)metric based on the literature for comparing the predictive performance of two models, called finite-time Kullback-Leibler (KL) rate. For any two general state space models with a general additive uncertainty, we first discuss an approach for propagating a general additive uncertainty (represented as a Gaussian Mixture Model (GMM) ) through a linear time-invariant system. We then apply this propagation approach to linear representations of nonlinear systems obtained through Dynamic Mode Decomposition (DMD). We illustrate this combined approach for the comparison of two lateral vehicle dynamics models over an obstacle avoidance maneuver to measure the effect of fidelity on the predictive performance of each model. We also apply this to a V&V problem, wherein we compare the application of the two vehicle dynamics models to reference vehicle trajectories. We see that the KL rate depends not only on the differences in the asymptotic spectral properties of each model, but also on the trajectory being simulated.Through these methods, we illustrate a process for comparing models of different fidelity over relevant trajectories. This enables us to understand which model is most appropriate for a particular scenario in a virtual test. We also show that the variation in the comparison metric can be connected to the phase space of the system. This enables us to use thresholds on the metric to decide when to switch between models of different fidelity.
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653 $a Additive uncertainty
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653 $a Bayesian propagation
653 $a Verification and validation
653 $a Virtual testing
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773 0 $t Dissertations Abstracts International $g 84-05B.
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791 $a Ph.D.
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793 $a English
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