基于学习的安全保证的滑动模式与相似的阵列机动控制四旋翼易受网络攻击的控制
Muhammad Maaruf1, Sami El-Ferik2
1Control and Instrumentation Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Interdisciplinary Research Centre for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2, AZ, UK.
ISA transactions
|February 25, 2025
概括
本研究介绍了一个安全的控制方案,用于网络四旋翼使用演员关键学习,以确保准确的形成机动,尽管干扰和网络攻击. 该方法保证了稳定性和约束满足,以实现强大的领导者-追随者控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 网络四旋翼机需要强大的控制来安全的形成机动.
- 外部干扰和网络攻击对控制系统性能和安全构成重大挑战.
- 确保在复杂的机动中满足动态状态约束是至关重要的.
研究的目的:
- 开发一个安全的领导者-追随者亲缘形成机动控制方案,用于网络四旋翼.
- 为了应对外部干扰,传感器欺骗和执行器注入攻击.
- 为了确保四旋翼状态保持在规定的安全约束范围内.
主要方法:
- 采用一个屏障Lyapunov功能来执行安全限制.
- 制定了一个分布式的滑动模式控制与演员-关键学习.
- 集成了一个Nussbaum增益功能,以抵消攻击诱导的控制增益腐败.
- 利用演员和评论员网络来估计不确定的动态,攻击和评估表现.
主要成果:
- 演员 - 关键学习方案有效地促进了准确的领导者 - 追随者相似形成机动.
- 控制方法成功地拒绝恶意网络攻击信号和外部干扰.
- 屏障Lyapunov功能确保安全限制始终保持.
- 利亚普诺夫稳定定理证实了闭环系统的局限稳定性.
结论:
- 提出的基于关键演员学习的控制方法确保了网络四旋翼的安全和准确的训练动作.
- 该方法证明了对各种干扰和复杂的网络攻击的弹性.
- 模拟结果验证了开发的控制策略的有效性和稳定性.
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