神经网络控制器的超快调节,用于自动驾驶汽车的路径跟踪
Zhihao Liang1, Kegang Zhao1, Junping Xie1
1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
ISA transactions
|May 1, 2024
概括
本研究介绍了一种基于模型的强化学习算法,用于使用最小的现实数据快速调整神经网络控制器. 该方法可以有效校准自动驾驶汽车路径跟踪,而环境相互作用有限.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 神经网络 (NN) 控制器为复杂的任务提供了先进的功能.
- 当前的NN控制器通常需要大量的数据集或频繁的环境交互,限制了现实世界的适用性.
研究的目的:
- 开发基于模型的强化学习 (MBRL) 算法,用于对深度NN控制器进行超快速调整.
- 为了从小的现实世界数据集中实现有效的控制器校准.
主要方法:
- 使用高斯过程 (GPs) 来建模未知的系统动态.
- 使用随机梯度下降来更新控制器参数.
- 包含基于粒子的方法来进行长期预测和在线状态估计器/过器.
主要成果:
- 成功校准了一个深度NN控制器,用于单次交互后自动驾驶汽车路径跟踪.
- 实现了与非线性模型预测控制 (NMPC) 方法可比的跟踪性能.
- 在各种测试场景中证明有效性,即使在部分可测量的状态和随机控制延迟的情况下.
结论:
- 拟议的MBRL算法显著减少了对NN控制器调的数据要求.
- 这种方法适合于现实世界的应用,如自动驾驶,提供高效和强大的控制.
- 能够从具有部分可观测性和控制不确定性的复杂系统中学习.
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