Modelado e identificación de vehículos móviles usando modelos de baja complejidad basados en datos
Autor: | Ignacio Carlucho, Mariano De Paula, Gerardo G. Acosta, Alejandro F. Rozenfeld |
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Rok vydání: | 2016 |
Předmět: |
0209 industrial biotechnology
Engineering Artificial neural network business.industry Control engineering 02 engineering and technology Systems modeling System dynamics Computer Science::Robotics Vehicle dynamics Kernel (linear algebra) Nonlinear system Identification (information) 020901 industrial engineering & automation Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
Zdroj: | 2016 IEEE Biennial Congress of Argentina (ARGENCON). |
Popis: | 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. |
Databáze: | OpenAIRE |
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