论文标题
攻击弹性观察者修剪轮式移动机器人的路径控制
Attack-resilient observer pruning for path-tracking control of Wheeled Mobile Robot
论文作者
论文摘要
车轮移动机器人（WMR）的路径跟踪控制引起了很多研究的关注，这主要是因为其广泛的适用性 - 例如智能轮椅，探索辅助的远程WMR。 WMR的远程和自主操作的最新增加导致控制循环中的IoT设备越来越多地使用。因此，通过虚假数据注射攻击（FDIA）为恶意相互作用提供界面。此外，已经证明基于优化的FDIA会在反馈控制系统中造成灾难性后果，同时借助任何基于残留的监控系统。由于这些攻击目标是系统测量过程，因此本文着重于提高动态观察者对FDIA的弹性的问题。具体而言，我们提出了一种攻击弹性的修剪算法，该算法试图将折衷的通道排除在观察者处理中。拟议的修剪算法将攻击精度提高到了$ 100 \％$，概率很高，这相应地提高了基础UKF对FDIA的弹性。通过FDIA下的差分驱动的轮式移动机器人（DDWMR）的两层路径跟踪控制平台的数值模拟，通过开发的基于弹性修剪的观察者进行的改进得到了验证。
Path-tracking control of wheeled mobile robot (WMR) has gained a lot of research attention, primarily because of its wide applicability -- for example intelligent wheelchairs, exploration-assistant remote WMR. Recent increase in remote and autonomous operations\requirements for WMR has led to more and more use of IoT devices within the control loop. Consequently, providing interfaces for malicious interactions through false data injection attacks (FDIA). Moreover, optimization-based FDIAs have been shown to cause catastrophic consequences in feedback control systems while by-passing any residual-based monitoring system. Since these attacks target system measurement process, this paper focuses on the problem of improving the resiliency of dynamical observers against FDIA. Specifically, we propose an attack-resilient pruning algorithm which attempts to exclude compromised channels from being processed by the observer. The proposed pruning algorithm improves attack-localization precision to $100\%$ with high probability, which correspondingly improves the resiliency of the underlying UKF to FDIA. The improvements due to the developed resilient pruning-based observer is validated through a numerical simulation of a two-layer path-tracking control platform of differential-driven wheeled mobile robot (DDWMR) under FDIA.
