TS-MPC for Autonomous Vehicle using a Learning Approach
This work addresses real-time control for autonomous vehicles in racing scenarios, but it is incremental as it combines existing methods like MPC, MHE, and ANFIS.
The paper tackled autonomous driving control by proposing a Model Predictive Control and Moving Horizon Estimator strategy that uses a data-driven Takagi-Sugeno model learned via ANFIS, achieving high driving performance in simulated racing environments.
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.