论文标题
使用学习方法的TS-MPC用于自动驾驶汽车
TS-MPC for Autonomous Vehicle using a Learning Approach
论文作者
论文摘要
在本文中，建议使用数据驱动的方法来学习车辆动力学的高海高吉诺（TS）表示，以实时解决自动驾驶控制问题。为了解决TS建模，我们使用自适应神经模糊推理系统（ANFIS）方法来获得一组基于多型的线性表示以及以非线性方式相关的一组成员资格函数。提出的控制方法是通过基于赛车的外部计划者的参考来提供的，以及MHE在赛车模式下提供高驾驶性能的估计。在模拟的赛车环境中测试了控制估计方案，以显示出提出的方法的潜力。
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.
