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Communication Dans Un Congrès Année : 2020

TS-MPC for Autonomous Vehicle using a Learning Approach

Résumé

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 different linear subsystems. The proposed control approach is provided by racing-based references of an external planner and estimations from the MHE offering 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.
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Dates et versions

hal-03132694 , version 1 (05-02-2021)

Identifiants

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Eugenio Alcala, Olivier Sename, Vicenç Puig, Joseba Quevedo. TS-MPC for Autonomous Vehicle using a Learning Approach. IFAC WC 2020 - 21st IFAC World Congress, Jul 2020, Berlin (virtual), Germany. ⟨10.1016/j.ifacol.2020.12.2034⟩. ⟨hal-03132694⟩
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