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.
@article{arxiv.2004.14362,
title = {TS-MPC for Autonomous Vehicle using a Learning Approach},
author = {Eugenio Alcalá and Olivier Sename and Vicenç Puig and Joseba Quevedo},
journal= {arXiv preprint arXiv:2004.14362},
year = {2020}
}