Číslo projektu:
Názov projektu:
Full-Authority Vehicle Control Strategy
Grantová schéma:
Dunajská stratégia 2019
Typ projektu:
Výskumné projekty APVV
Začiatok projektu:
Koniec projektu:
Zodpovedný riešiteľ:
Martin Klaučo
Matúš Furka, Karol Kiš, Michal Kvasnica

Joining their research backgrounds, the three involved research institutes will collaborate and exchange on developing and applying nonlinear optimal control methods (nonlinear model-predictive control formulations, estimation problems, parameter identification problems) to industrial application settings in the area of vehicle dynamics. Control tasks that have not been feasible before (e.g., highly-integrated nonlinear drive train optimization) seem feasible by extending and applying nonlinear explicit model-predictive control tools. When successful, these tools allow complex, nonlinear, constraint optimal control to be computed fast enough for realtime control on cheap hardware.



  1. D. Efremov – M. Klaučo – T. Haniš: Driving Envelope: On Vehicle Stability Through Tire Capacities. V IEEE INTELLIGENT VEHICLES SYMPOSIUM, IEEE, str. 1188–1193, 2022.


  1. D. Efremov – T. Haniš – M. Klaučo: Haptic Driver Guidance for Lateral Driving Envelope Protection Using Model Predictive Control. V IEEE Intelligent Vehicles Symposium, IEEE Xplore, Las Vegas, NV, USA, USA, 2021.
  2. M. FurkaK. KišP. BakaráčM. Klaučo: Nonlinear MPC Policy for Systems with Data Driven Identification. V Proceedings of the 7th IFAC Conference on Nonlinear Model Predictive Control, IFAC-PapersOnline, č. 54, 2021.
  3. K. KišM. KlaučoM. Kvasnica: Explicit MPC in the form of Sparse Neural Networks. Editor(i): R. Paulen and M. Fikar, V Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, str. 163–168, 2021.


Zodpovednosť za obsah: doc. Ing. MSc. Martin Klaučo, PhD.
Posledná aktualizácia: 11.05.2020 15:51
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