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master's thesis
Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:634326
Marić, Ante
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Marić, A. (2023). Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:634326

Marić, Ante. "Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:634326

Marić, Ante. "Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:634326

Marić, A. (2023). 'Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:634326

Marić A. Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:634326

A. Marić, "Ponavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:634326

Metadata
TitlePonavljajuće neuronske mreže i Gaussovi procesi za modeliranje nelinearne dinamike za modelsko prediktivno upravljanje
Title (english)Recurrent neural networks and Gaussian processes for modeling of nonlinear dynamics for model predictive control
AuthorAnte Marić
MentorIvan Petrović (mentor)
Committee memberIvan Petrović (član povjerenstva)
Committee memberIvan Marković (član povjerenstva)
Committee memberLuka Petrović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-02-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Modelsko prediktivno upravljanje (MPC) moćna je metoda optimalnog upravljanja koja se oslanja na model upravljanoga sustava. Za nelinearne dinamičke sustave, konvencionalne tehnike modeliranja mogu postati neprimjenjive zbog složenog ponašanja i visokodimenzionalnog prostora stanja. Recentna istraživanja zbog toga su usmjerena na uključivanje modela strojnog učenja i dubokog učenja u MPC. Ovaj rad implementira metodu modeliranja dinamičkih sustava pomoću Gaussovih procesa i uspoređuje ju s Ponavljajućim neuronskim mrežama. Modeli su implementirani s regulatorom baziranim na modelskom prediktivnom integralu puta. Upravljačke sposobnosti uspoređene su s drugim regulatorima na simuliranom i fizičkom probnom sustavu. Dodatno, demonstrirana je mogućnost upravljanja fizičkim sustavom u stvarnom vremenu koristeći implementirani GP model treniran na maloj količini simuliranih podataka.
Abstract (english)
Model predictive control (MPC) is a powerful optimal control method that relies on the model of the system that is being controlled. For nonlinear dynamical systems, conventional modeling techniques can become infeasible due to complex behavior and high-dimensional state-space. Because of this, recent research has been focused on incorporating machine learning and deep learning models with MPC. This work implements a Gaussian process (GP) modeling method and compares it to Recurrent neural networks (RNNs). Both models are implemented with a Model predictive path integral (MPPI) controller and tested against other controllers on a simulated and physical example system. Furthermore, it is shown that the implemented GP method is viable for real-time MPPI control of a physical system when trained on small amounts of simulated data.
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Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:634326
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 on2023-07-13 10:18:36