数据驱动的建模和自适应事件触发的安全控制,用于受传感器攻击的自动驾驶汽车
Hong-Tao Sun1, Xinyu Xie1, Miao Rong2
1College of Engineering, Qufu Normal University, Rizhao, China.
ISA transactions
|October 8, 2025
概括
本研究介绍了一种基于数据的方法,用于安全控制自动驾驶汽车,解决传感器攻击. 适应性事件触发方案提高了通信效率和对网络威胁的控制性能.
科学领域:
- 控制工程 控制工程 控制工程
- 网络安全 网络安全
- 自主系统 自主系统
背景情况:
- 自动驾驶汽车面临来自传感器攻击的漏洞,危及安全性和性能.
- 传统的控制方法与现实世界传感器攻击和数据驱动建模的复杂性作斗争.
研究的目的:
- 开发针对传感器攻击的自动驾驶汽车数据驱动的安全控制策略.
- 使用适应性事件触发机制来提高通信效率和控制性能.
主要方法:
- 动态模式分解 (DMD) 用于数据驱动的侧向车辆模型识别.
- 适应性事件触发控制方案,以优化通信和性能.
- 类似滑动模式的控制以抵消传感器攻击.
- 连普诺夫理论和线性矩阵不等式 (LMIs) 用于稳定性分析.
主要成果:
- 从使用DMD的数据中成功识别了自动驾驶车辆动态.
- 开发一个适应性事件触发方案,平衡通信负载和控制效率.
- 通过拟议的控制策略,证明有效地减轻传感器攻击.
- 通过比较模拟来验证控制方案的有效性.
结论:
- 拟议的数据驱动方法有效地解决了自动驾驶汽车中的传感器攻击.
- DMD简化了模型识别,而自适应事件触发控制提高了系统效率.
- 安全控制方案提供了一个强大的解决方案,以提高自动驾驶的安全性和可靠性.
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