使用神经网络自适应估计器方法连接自动驾驶汽车的规定的时间可靠的车队控制
Balakrishnan Devanathan1,2, Palanisamy Selvaraj3, Arumugam Suyampulingam4
1Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India.
Scientific reports
|February 25, 2025
概括
本研究涉及在不确定的动态和随机执行器故障下连接的自动驾驶汽车队列控制. 一个强大的自适应观察员可以提高控制系统的稳定性和性能,并通过模拟验证.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 互联自动驾驶汽车 (CAV) 提出了复杂的控制挑战.
- Platooning 需要强大的控制策略来管理不确定性.
- 随机执行器故障可能会损害车辆安全和系统完整性.
研究的目的:
- 为CAVs制定一个规定的时间排队控制策略.
- 为了解决未知的车辆动力学和随机执行器故障.
- 为了提高控制系统的稳定性和稳定性.
主要方法:
- 利用基于辐射的函数的自适应观察器来对未知的动力学进行补偿.
- 采用伯努利分布的内置随机执行器故障建模.
- 应用了利亚普诺夫稳定理论和随机分析,用于系统验证.
主要成果:
- 拟议的控制算法证明了对未知动态的强化稳定性.
- 该系统有效地弥补了随机执行器故障.
- 模拟结果证实了规定的时间控制设计的稳定性和有效性.
结论:
- 开发的基于观察者的自适应控制对CAV排队有效.
- 该方法为存在不确定性和故障的系统提供了强大的解决方案.
- 这项研究有助于实现更安全,更可靠的自动驾驶汽车运行.
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