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arXiv · 2004.14362

TS-MPC for Autonomous Vehicle using a Learning Approach

Eugenio Alcalá·Olivier Sename·Vicenç Puig·Joseba Quevedo
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Abstract

In this paper, the 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 presented approaches.

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BibTeXRIS

Eugenio Alcalá, Olivier Sename, Vicenç Puig, Joseba Quevedo. 2020-04-29. TS-MPC for Autonomous Vehicle using a Learning Approach. https://arxiv.org/abs/2004.14362

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