无模型的自适应型代学习控制,具有测量干扰和数据丢失,用于自动公交车轨迹跟踪系统的自主公交车轨迹跟踪系统
Shida Liu1, Wei Huang1, Ye Ren1
1The North China University of Technology's School of Electrical and Control Engineering, Beijing, China.
Science progress
|March 17, 2024
概括
这项研究引入了一种针对自动驾驶公共汽车的增强型无模型自适应代学习控制 (MF-AILC),对随机数据丢失具有稳定性. 新方法确保了可靠的轨迹跟踪,尽管数据丢失,只使用输入/输出数据.
科学领域:
- 控制工程 控制工程 控制工程
- 机器人技术 机器人技术 机器人技术
- 自主系统 自主系统
背景情况:
- 自动驾驶系统,如公共汽车,需要精确的轨迹跟踪,以确保安全高效的运行.
- 测量中断和随机数据丢失对控制系统性能构成重大挑战.
- 现有的控制方法可能会与间歇性数据可用性作斗争,影响可靠性.
研究的目的:
- 为自动公交车轨迹跟踪开发一种增强的无模型自适应代学习控制 (MF-AILC) 方法.
- 为应对随机数据丢失和控制系统中测量中断所带来的挑战.
- 为了确保稳健和高性能的轨迹跟踪,即使有不完整的数据.
主要方法:
- 实施了一个增强的MF-AILC控制器,其中包含了一个新的数据补偿机制.
- 使用数据驱动的控制策略,仅依靠输入/输出数据来设计控制器.
- 严格的数学分析来验证拟议的控制算法的收性质.
主要成果:
- 拟议的MF-AILC方法有效地弥补了缺失的数据,减轻了随机学的影响.
- 在自动驾驶巴士轨迹跟踪模拟中表现出卓越的控制性能.
- 通过数学证明验证了数据补偿机制的收性和有效性.
结论:
- 增强的MF-AILC提供了一个强大的解决方案,用于在数据丢失的情况下自动追踪总线轨迹.
- 数据驱动的方法通过消除对系统模型的需求来简化控制器设计.
- 这种方法为可靠的自动驾驶汽车控制系统提供了有前途的进步.
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