prikaz prve stranice dokumenta Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža
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master's thesis
Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:465583
Jarak, Nikolina
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Jarak, N. (2021). Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:465583

Jarak, Nikolina. "Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:465583

Jarak, Nikolina. "Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:465583

Jarak, N. (2021). 'Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:465583

Jarak N. Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:465583

N. Jarak, "Identifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:465583

Metadata
TitleIdentifikacija modela vozila temeljenog na Koopmanovom operatoru korištenjem dubokih neuronskih mreža
Title (english)Identification of the vehicle model based on Koopman operator using deep neural networks
AuthorNikolina Jarak
MentorJadranko Matuško (mentor)
Committee memberJadranko Matuško (predsjednik povjerenstva)
Committee memberŠandor Ileš (član povjerenstva)
Committee memberŽeljko Jakopović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Model vozila predstavlja primjer dinamički nelinearmog sustava. U ovom radu opisane su duboke neuronske mreže te Koopmanov operator koji je zasnovan na algoritmu dinamičke modalne dekompozicije s ciljem identifikacije modela vozila te u konacnosti razvijanja modela upravljanja. Prema tome, opisana je i metoda upravljanja LQR regulatorom. Zbog složene nelinearne dinamike modela vozila, neuronska mreža je kreirana u tri dijela: enkoder, latentni dio mreže koji sadržava Koopmanov operator te dekoder. Koopmanov operator je naucen koristeći algoritam dinamičke modalne dekompozicije zasnovan na dubokoj neuronskoj mreži. Duboka neuronska mreža je implementirana tako što je prvo podignuta dimenzija prostora stanja, naucen je Ko- opmanov operator zajedno s neuronskom mrežom te su na kraju opet rekonstruirane varijable stanja u sljedecem koraku. Podaci za učenje duboke neuronske mreže su iz- vuceni iz CarMaker-a te je složeno sučelje unutar simulacijskog okruženja kako bi se mogao istestirati LQR regulator. Usporedeni su dobiveni podaci iz neuronske mreže sa stvarnim podacima te analizirani rezultati testiranja LQR regulatora.
Abstract (english)
The vehicle model is an example of a dynamically nonlinear system. In this paper deep neural networks and the Koopman operator based on the dynamical modal decomposition algorithm with the aim of identifying vehicle model and developing controller. Therefore, the control method with LQR regulator is also described. Due to the comples nonlinear dynamics of the vehicle model, the neural network is created in three parts: the encoder, the latent part of the network containing the Koopman operator and the decoder. The Koopman operator was learned using the dynamic modal decomposition algorithm based on a deep neural network. Deep neural network is implemented by first augmenting the dimension of the state space, Koopman operator was learned parallel with the neural network and were reconstructed again state variables in next step. Deep neural network learning data was extracted from CarMaker and the interface was set up within the simulation environment to could test the LQR regulator. The obtained data from the neural network were compared with actual data and analyzed test results of LQR regulator.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:465583
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 15:08:47