TS-MPC for Autonomous Vehicle using a Learning Approach

• URL: http://arxiv.org/abs/2004.14362v1
• Date: Wed, 29 Apr 2020 17:42:33 GMT
• Title: TS-MPC for Autonomous Vehicle using a Learning Approach
• Authors: Eugenio Alcal\'a, Olivier Sename, Vicen\c{c} Puig, and Joseba Quevedo
• Abstract summary: We use a data-driven approach to learn a Takagi-Sugeno (TS) representation of the vehicle dynamics. To address the TS modeling, we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach. The proposed control approach is provided by racing-based references of an external planner and estimations from the MHE.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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