This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car.

中文翻译：

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使用自适应神经模糊推理系统和线性矩阵不等式方法对自动驾驶车辆进行基于学习的控制

本文提出了一种基于学习的自动驾驶车辆控制方法。使用来自预先存在的控制器的输入和输出数据，采用自适应神经模糊推理系统（ANFIS）算法来学习显式 Takagi-Sugeno（TS）控制器。同时，利用Lyapunov理论和LMI将车辆模型识别为TS模型形式，用于闭环稳定性评估。所提出的方法用于从 MPC 控制器学习控制律，从而避免使用在线优化。这减少了控制环路的计算负担并有利于实时实现。最后，通过使用小型自动赛车进行模拟来评估所提出的方法。