面向轮式移动机器人路径跟踪控制的抗攻击观测器剪枝方法
Attack-resilient observer pruning for path-tracking control of Wheeled Mobile Robot
AI总结：
针对轮式移动机器人路径跟踪控制中虚假数据注入攻击绕过残差监控的问题，提出抗攻击观测器剪枝算法，排除受攻陷通道，以高概率实现100%攻击定位精度，增强UKF抗攻击能力，并通过DDWMR双层平台仿真验证有效性。
摘要：
轮式移动机器人（WMR）的路径跟踪控制因应用场景广泛（如智能轮椅、辅助勘探的远程WMR）受到大量研究关注。近期WMR对远程与自主运行需求的增长，使得控制回路中越来越多地使用物联网设备，进而为虚假数据注入攻击（FDIA）的恶意交互提供了接口。此外，已有研究表明，基于优化的FDIA可绕过所有基于残差的监控系统，给反馈控制系统造成灾难性后果。由于这类攻击针对系统测量过程，本文聚焦于提升动态观测器抵御FDIA的韧性问题。具体而言，我们提出一种抗攻击剪枝算法，旨在将受攻陷的通道排除在观测器处理范围之外。所提剪枝算法以高概率将攻击定位精度提升至100%，相应增强了底层无迹卡尔曼滤波（UKF）对FDIA的抵御能力。我们通过在FDIA场景下对差速驱动轮式移动机器人（DDWMR）的双层路径跟踪控制平台进行数值仿真，验证了所开发的基于韧性剪枝的观测器带来的性能提升。
英文摘要：
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.
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