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dc.contributor.authorFan, Fletcher
dc.date.accessioned2023-08-08T06:09:47Z
dc.date.available2023-08-08T06:09:47Z
dc.date.issued2023en
dc.identifier.urihttps://hdl.handle.net/2123/31538
dc.description.abstractLearning models of dynamical systems from data is a widely-studied problem in control theory and machine learning. One recent approach for modelling nonlinear systems considers the class of Koopman models, which embeds the nonlinear dynamics in a higher-dimensional linear subspace. Learning a Koopman embedding would allow for the analysis and control of nonlinear systems using tools from linear systems theory. Many recent methods have been proposed for data-driven learning of such Koopman embeddings, but most of these methods do not consider the stability of the Koopman model. Stability is an important and desirable property for models of dynamical systems. Unstable models tend to be non-robust to input perturbations and can produce unbounded outputs, which are both undesirable when the model is used for prediction and control. In addition, recent work has shown that stability guarantees may act as a regularizer for model fitting. As such, a natural direction would be to construct Koopman models with inherent stability guarantees. Two new classes of Koopman models are proposed that bridge the gap between Koopman-based methods and learning stable nonlinear models. The first model class is guaranteed to be stable, while the second is guaranteed to be stabilizable with an explicit stabilizing controller that renders the model stable in closed-loop. Furthermore, these models are unconstrained in their parameter sets, thereby enabling efficient optimization via gradient-based methods. Theoretical connections between the stability of Koopman models and forms of nonlinear stability such as contraction are established. To demonstrate the effect of the stability guarantees, the stable Koopman model is applied to a system identification problem, while the stabilizable model is applied to an imitation learning problem. Experimental results show empirically that the proposed models achieve better performance over prior methods without stability guarantees.en
dc.rightsCopyright All Rights Reserveden
dc.subjectSystem Identificationen
dc.subjectMachine Learningen
dc.subjectKoopman Methodsen
dc.subjectImitation Learningen
dc.titleLearning Stable Koopman Models for Identification and Control of Dynamical Systemsen
dc.typeThesis
dc.type.thesisDoctor of Philosophyen
dc.rights.otherThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en
usyd.facultySeS faculties schools::Faculty of Engineering::School of Aerospace Mechanical and Mechatronic Engineeringen
usyd.degreeDoctor of Philosophy Ph.D.en
usyd.awardinginstThe University of Sydneyen
usyd.advisorRye, Daviden


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