强大的非线性模型预测控制,以触发干扰拒绝事件,对未经测速的无人驾驶地面车辆进行DoS攻击
Na Feng1, Defeng Wu2, Hongliang Yu3
1School of Marine Engineering, Jimei University, Xiamen 361021, PR China.
ISA transactions
|August 29, 2025
概括
这项研究引入了无人地面车辆 (USV) 的新控制方法,即使在拒绝服务 (DoS) 攻击和有限的传感器数据中,也提高了跟踪精度和效率.
科学领域:
- 机器人和控制系统
- 海洋工程
- 自主系统的网络安全
背景情况:
- 由于有限的控制输入,无人驾驶地面车辆 (USV) 面临精确导航的挑战.
- 拒绝服务 (DoS) 攻击对自主船舶系统的运行完整性和性能构成重大威胁.
- 由于缺乏准确的速度测量,使得执行有效的USV控制策略变得复杂.
研究的目的:
- 开发一种可以拒绝干扰并保持高精度跟踪的USV先进控制方法.
- 通过结合强大的防御机制来解决USV对DoS攻击的脆弱性.
- 通过事件触发方法提高控制系统的计算效率.
主要方法:
- 设计了一个干扰排斥事件触发的非线性模型预测控制 (DR-ETNMPC) 策略,用于低值的USV.
- 使用非线性扩展状态观察器 (NESO) 来估计未知速度和块性干扰.
- 一个事件触发机制和DoS攻击防御机制被整合起来以优化计算并确保安全性.
主要成果:
- 拟议的DR-ETNMPC方法证明了有效的干扰排斥和精确的USV轨迹跟踪.
- 即使在模拟的DoS攻击中,系统也保持了高性能控制,并展现了弹性.
- 由于事件触发的实现,人们观察到计算效率的显著提高.
结论:
- 开发的DR-ETNMPC方法提供了一个强大而高效的解决方案,用于在具有挑战性的环境中控制低功率的USV.
- 整合NESO和事件触发可以提高控制精度并减少计算负担.
- 在网络威胁下,DoS攻击防御机制确保可靠的操作和高精度的跟踪性能.
相关概念视频
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