Learning-Based Model Predictive Control for Autonomous Racing
Learning-Based Model Predictive Control for Autonomous Racing
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DOI：
10.3390/wevj14070163
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发表时间：
2023-06
影响因子：
2.3
通讯作者：
João Pinho;Gabriel Costa;Pedro U. Lima;Miguel Ayala Botto
中科院分区：
文献类型：
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作者：
João Pinho;Gabriel Costa;Pedro U. Lima;Miguel Ayala Botto
在本文中，我们提出了自适应的终端组件学习为基础的模型预测控制（TC-LMPC）架构的自主赛车的学生方程式无人驾驶（FSD）上下文。我们测试的TC-LMPC架构，一个无参考的控制器，能够从以前的迭代中学习，通过建立一个适当的终端安全设置和终端成本从收集的轨迹和输入序列，在专用于FSD比赛的车辆模拟器。自动驾驶赛车的一个主要问题是难以获得覆盖整个性能包络的精确的高度非线性车辆模型。当控制者推动越来越激进的行为时，这种情况会更加严重。为了解决这个问题，我们使用离线和在线测量和机器学习（ML）技术来在线适应车辆模型。我们测试了两个稀疏高斯过程回归（GPR）近似模型学习。模型学习部分的新奇在于使用了一种用于初始训练数据集的选择方法，该方法最大化了信息增益标准。具有模型学习功能的TC-LMPC在10圈FSD比赛中缩短了5.9 s（3%）。
In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time.