Print Email Facebook Twitter Koopman Subspace Identification in the Presence of Measurement Noise Title Koopman Subspace Identification in the Presence of Measurement Noise Author Borghi, Alessandro (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Wahls, S. (mentor) Mazo, M. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2021-09-30 Abstract The ability to compute models that correctly predict the trajectories of a nonlinear system can become a significant challenge in systems and control. The introduction of Koopman operator theory helped to deal with this challenge. The Koopman operator is a composition operator that globally describes a nonlinear system in an infinite-dimensional linear framework. To implement this theory, the usual approach is to approximate the Koopman operator through data-driven methods. These algorithms use measurements of the nonlinear system to compute the approximated operator. Generally, noise can be present in real-world scenarios. Noisy measurements can have a considerable deteriorating effect on the data-driven approximation of Koopman operators. The approximation of this operator in presence of noisy training data is a necessary step for its implementation to a wider spectrum of real-world applications. Many robust numerical methods were designed to solve this issue. Koopman subspace identification (KSI) is a promising approach. As the name suggests, this algorithm employs subspace identification modeling to compute the matrix approximation of the Koopman operator. In this work, we test KSI against other state-of-the-art techniques. Additionally, we improve its performance in predicting the state trajectories of the nonlinear system in presence of noisy measurements. To this end, we propose a reducing-order routine that computes the most robust model against measurement noise. Furthermore, a randomized singular value decomposition is adopted to reduce computational times. The improved KSI is then compared against the other state-of-the-art algorithms in the presence of noisy data sets. We will show that the upgraded KSI outperforms most of the other techniques. Subject Koopman OperatorSubspace IdentificationMeasurement NoiseData-Driven Modelling To reference this document use: http://resolver.tudelft.nl/uuid:22250d5c-875c-44a9-adf4-d643a6a08dba Part of collection Student theses Document type master thesis Rights © 2021 Alessandro Borghi Files PDF Thesis_Alessandro_Borghi.pdf 3.7 MB Close viewer /islandora/object/uuid:22250d5c-875c-44a9-adf4-d643a6a08dba/datastream/OBJ/view