Dataset: bicycle-model-koopman-master.zip, 36.95 MB Access Condition: Open access Description: Koopman based MPC of bicycle vehicle model. The code was used to create the paper "Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition (English)
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Cite this document
Ileš, Š., Švec, M. & Matuško, J. (2024). Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition [Data set]. https://urn.nsk.hr/urn:nbn:hr:168:985912.
Ileš, Šandor, et al. Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition. Fakultet elektrotehnike i računarstva, 2024. 30 Jul 2024. https://urn.nsk.hr/urn:nbn:hr:168:985912.
Ileš, Šandor, Marko Švec, and Jadranko Matuško. 2024. Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition. Fakultet elektrotehnike i računarstva. https://urn.nsk.hr/urn:nbn:hr:168:985912.
Ileš, Š., Švec, M. and Matuško, J. 2024. Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition. Fakultet elektrotehnike i računarstva. [Online]. [Accessed 30 July 2024]. Available from: https://urn.nsk.hr/urn:nbn:hr:168:985912.
Ileš Š, Švec M, Matuško J. Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition. [Internet]. Fakultet elektrotehnike i računarstva; 2024, [cited 2024 July 30] Available from: https://urn.nsk.hr/urn:nbn:hr:168:985912.
Š. Ileš, M. Švec and J. Matuško, Model predictive control of vehicle dynamics based on the Koopman operator with extended dynamic mode decomposition, Fakultet elektrotehnike i računarstva, 2024. Accessed on: Jul 30, 2024. Available: https://urn.nsk.hr/urn:nbn:hr:168:985912.
A novel approach to solving the problem of controlling nonlinear systems is based on the so-called Koopman operator. The Koopman operator is a linear operator that governs the evolution of scalar functions (often referred to as observables) along the trajectories of a given nonlinear dynamical system and is a powerful tool for the analysis and decomposition of nonlinear dynamical systems. The main idea is to lift the nonlinear dynamics to a higher dimensional space where its evolution can be described with a linear system model. This dataset is used to create a model predictive controller for vehicle dynamics based on the Kooopman operator decomposition of vehicle dynamics with Extended Dynamic Mode Decomposition (EDMD) method.
Methods (english)
Model identification and predictive controller design are designed using Matlab/Simulink environment.