强大的适应性H∞耐故障预测控制,用于基于RBF-DNN近似的空地集成高速公路应急自组织网络系统
Zhifang Wang1, Yingjian Wang2, Sen Tian3
1Henan Police College Intelligent Investigation Center, No. 1 Longzihu East Road Jinshui District, Zhengzhou 450046, China; Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China.
ISA transactions
|February 25, 2026
概括
本研究介绍了一种强大的适应性控制方法,用于高速公路紧急情况中的空地网络. 新方法通过提高通信可靠性和预测事故风险来提高道路交通安全.
科学领域:
- 控制系统工程 控制系统工程
- 网络化系统 网络化系统
- 交通运输中的人工智能
背景情况:
- 高速公路紧急情况需要在空地集成无线特设网络中进行强大的通信和决策支持.
- 现有的容错控制方案难以应对时间变化的道,数据包丢失和组件退化等合挑战.
- 预测事故风险对于主动安全管理至关重要.
研究的目的:
- 为双层异质"UAV-road"网络提出一个强大的适应性H∞耐故障的预测控制方法.
- 将容错性,通信约束和事故风险预测整合到一个统一的框架中.
- 加强道路交通安全和应急响应能力.
主要方法:
- 开发了一个网络控制模型,包括链接质量,队列演变和资源限制.
- 采用一个射线基函数深度神经网络 (RBF-DNN) 来在线近似系统非线性.
- 设计了一个具有投射和σ-修改的自适应控制规律,加上一个事件触发的通信机制.
- 使用Lyapunov框架来建立系统稳定性和H∞性能极限.
主要成果:
- 拟议的控制器在恶劣条件下 (链路中断,40-50%的数据包丢失,75%的执行器退化) 保持了服务质量 (QoS) 和H∞性能.
- 在闭环系统中实现了非对称状态的融合.
- 事故风险预测模块在真实高速公路数据上显示了70-75%的测试准确性,PR-AUC为0.683和ROC-AUC为0.715.
结论:
- 提出的强大的适应性H∞耐故障的预测控制方法有效地解决了公路安全的空地网络的复杂挑战.
- 综合方法提供可靠的控制和准确的事故风险预测,确认工程可行性.
- 该框架显著提高了在高速公路紧急情况期间公共安全治理的决策支持.
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