TS-MPC for autonomous vehicle using a learning approach
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hdl:2117/341522
Tipus de documentText en actes de congrés
Data publicació2020
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Abstract
In this paper, Model Predictive Control (MPC) and Moving Horizon Estimator (MHE) strategies using a data-driven approach to learn a Takagi-Sugeno (TS) representation of the vehicle dynamics are proposed to solve autonomous driving control problems in real-time. To address the TS modeling, we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to obtain a set of polytopic-based linear representations as well as a set of membership functions relating in a non-linear way the di erent linear subsystems. The proposed control approach is provided by racing-based references of an external planner and estimations from the MHE o ering a high driving performance in racing mode. The control-estimation scheme is tested in
a simulated racing environment to show the potential of the proposed approaches.
CitacióAlcala, E. [et al.]. TS-MPC for autonomous vehicle using a learning approach. A: World Congress of the International Federation of Automatic Control. "IFAC 2020 - 21th World Congress of the International Federation of Automatic Control: Berlin, Germany". 2020, p. 1-6.
Versió de l'editorhttp://www.sciencedirect.com/science/journal/24058963IFAC-PapersOnLine
Altres identificadorshttps://arxiv.org/abs/2004.14362
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2271.pdf | postprint | 1,751Mb | Visualitza/Obre |