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METHODS COMPARISON ON FLOW MODEL CONSTRUCTION AND PARAMETER ESTIMATION

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Abstract

Knowing the equation of an unknown dynamical system is essential when trying to apply optimal control. Sometimes researchers do not have a comprehensive knowledge to a nonlinear system. The unknown part might be the function representing the relation between states (e.g. transfer function), or key parameters of a dynamical system (e.g. proportional constant of spring in a linear spring system). Various methods have been developed to identify the dynamics of an unknown system. In this thesis, multiple approaches include Neural Network polynomial Extraction (NN-poly), Sparse Identification of nonlinear Dynamics (SINDy) and Non-Uniform Discrete Fourier Transform (NUDFT) are compared over their ability to find the expression of unknown systems or to estimate key parameters of a dynamical system. Multiple tasks with different purposes are created to test the performances of these methods.

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67 pages

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2020-08

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Ferrari, Silvia

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Petersen, Kirstin Hagelskjaer

Degree Discipline

Mechanical Engineering

Degree Name

M.S., Mechanical Engineering

Degree Level

Master of Science

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Government Document

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dissertation or thesis

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