This book showcases a collection of papers that present cutting-edge studies, methods, experiments, and applications in various interdisciplinary fields. These fields encompass optimal control, guidance, navigation, game theory, stability, nonlinear dynamics, robotics, sensor fusion, machine learning, and autonomy. The chapters reveal novel studies and methods, providing fresh insights into the field of optimal guidance and control for autonomous systems. The book also covers a wide range of relevant applications, showcasing how optimal guidance and control techniques can be effectively applied in various domains, including mechanical and aerospace engineering. From robotics to sensor fusion and machine learning, the papers explore the practical implications of these techniques and methodologies.

Koopman operator based modeling and control of quadrotors / Martini, Simone; Rizzo, Alessandro; Stefanovic, Margareta; Livreri, Patrizia; Rutherford, Matthew; Valavanis, Kimon. - 40:(2024), pp. 253-266. (Intervento presentato al convegno 2023 IUTAM Symposium on Optimal Guidance and Control for Autonomous Systems) [10.1007/978-3-031-39303-7].

Koopman operator based modeling and control of quadrotors

Rizzo, Alessandro;
2024

Abstract

This book showcases a collection of papers that present cutting-edge studies, methods, experiments, and applications in various interdisciplinary fields. These fields encompass optimal control, guidance, navigation, game theory, stability, nonlinear dynamics, robotics, sensor fusion, machine learning, and autonomy. The chapters reveal novel studies and methods, providing fresh insights into the field of optimal guidance and control for autonomous systems. The book also covers a wide range of relevant applications, showcasing how optimal guidance and control techniques can be effectively applied in various domains, including mechanical and aerospace engineering. From robotics to sensor fusion and machine learning, the papers explore the practical implications of these techniques and methodologies.
2024
978-3-031-39302-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2985383