Modelado e identificación de vehículos móviles usando modelos de baja complejidad basados en datos

Mariano De Paula, Ignacio Carlucho, Alejandro Rozenfeld, Gerardo G. Acosta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Autonomous vehicles are attractive platforms for several applications such as aerial, terrestrial, aquatic and underwater applications. The system modeling and identification is paramount to the success of the model-based controllers. Reliable control strategies require faithful models to achieve a good performance. Classical modeling represents the system dynamics by ordinary differential equations and often requires extensive human knowledge. Many times, the dynamics are complex and nonlinear and also many simplification assumptions are made during system modeling. In this paper we compare different data-driven techniques to model the system dynamics. Particularly, we use the well-known artificial neural networks, multilayer perceptron and radial basis functions, as well as Gaussian process regression to model the vehicles dynamics. These techniques learn the underlying structure of the vehicles dynamics from the experimentally measured data offering a natural framework to incorporate the unknown nonlinearities. In this paper a terrestrial vehicle is identified, the Pioneer 3 at and the obtained model is validated with the real vehicle.
Original languageSpanish
Title of host publication2016 IEEE Biennial Congress of Argentina (ARGENCON)
Number of pages6
ISBN (Electronic)978-1-4673-9765-0, 978-1-4673-9764-3
ISBN (Print)978-1-4673-9766-7
Publication statusPublished - 10 Oct 2016
Event2016 Biennial Congress of IEEE Argentina (ARGENCON)
- Buenos Aires, Argentina
Duration: 15 Jun 201617 Jun 2016


Conference2016 Biennial Congress of IEEE Argentina (ARGENCON)
Abbreviated titleARGENCON 2016
CityBuenos Aires

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