More Like this

1. Perfectly Undetectable Reflection and Scaling False Data Injection Attacks via Affine Transformation on Mobile Robot Trajectory Tracking Control

   https://doi.org/10.1109/TRO.2025.3626620  

   Ueda, Jun; Kwon, Hyukbin (October 2025, IEEE Transactions on Robotics)

   With the increasing integration of cyber-physical systems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade system performance while remaining undetected. This article investigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a nonholonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the partially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are validated through experiments using a Turtlebot 3 platform, highlighting the urgent need for sophisticated detection mechanisms and resilient control strategies to safeguard CPS against such threats. Furthermore, a novel approach for detection of these attacks called the state monitoring signature function (SMSF) is introduced. An example SMSF, a carefully designed function resilient to FDIA, is shown to be able to detect the presence of an FDIA through signatures based on system states. 

   more » « less
2. Distributed Detection of Adversarial Attacks for Resilient Cooperation of Multi-Robot Systems with Intermittent Communication

   Bahrami, Rayan; Jafarnejadsani, Hamidreza (October 2024, IEEE transactions on control of network systems)

   This paper concerns the consensus and formation of a network of mobile autonomous agents in adversarial settings where a group of malicious (compromised) agents are subject to deception attacks. In addition, the communication network is arbitrarily time-varying and subject to intermittent connections, possibly imposed by denial-of-service (DoS) attacks. We provide explicit bounds for network connectivity in an integral sense, enabling the characterization of the system’s resilience to specific classes of adversarial attacks. We also show that under the condition of connectivity in an integral sense uniformly in time, the system is finite-gain L stable and uniformly exponentially fast consensus and formation are achievable, provided malicious agents are detected and isolated from the network. We present a distributed and reconfigurable framework with theoretical guarantees for detecting malicious agents, allowing for the resilient cooperation of the remaining cooperative agents. Simulation studies are provided to illustrate the theoretical findings. 

   more » « less
3. Resilient Control Under Denial-of-Service and Uncertainty: An Adaptive Dynamic Programming Approach

   https://doi.org/10.1109/TAC.2025.3527305  

   Gao, Weinan; Jiang, Zhong-Ping; Chai, Tianyou (June 2025, IEEE Transactions on Automatic Control)

   In this article, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty. 

   more » « less
4. A Reinforcement Learning-Based Parameter Tuning Approach for a Secure Cooperative Adaptive Cruise Control System

   https://doi.org/10.4271/12-08-04-0033  

   Javidi-Niroumand, Farahnaz; Sargolzaei, Arman (December 2025, SAE International Journal of Connected and Automated Vehicles)

   Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the controller, observer, and FDI attack estimator, ensuring the safety and security of the developed CACC under varying conditions. We evaluated the performance of the developed controller before and after optimizing parameters, and the results show about a 50% improvement in accuracy of the FDI attack estimation and a 76% enhancement in safe following distance with the optimized controller in each scenario. 

   more » « less
5. The Convex Uncertain Voronoi Diagram for Safe Multi-Robot Multi-Target Tracking Under Localization Uncertainty

   https://doi.org/10.1007/s10846-023-01986-0  

   Chen, Jun; Dames, Philip (December 2023, Journal of Intelligent & Robotic Systems)

   Accurately detecting, localizing, and tracking an unknown and time-varying number of dynamic targets using a team of mobile robots is a challenging problem that requires robots to reason about the uncertainties in their collected measurements. The problem is made more challenging when robots are uncertain about their own states, as this makes it difficult to both collectively localize targets and avoid collisions with one another. In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot. Our algorithms are capable of safely driving mobile robots towards areas of high information distribution while maintaining coverage of the whole area of interest. We demonstrate the efficacy of these algorithms via a series of simulated and hardware tests, and compare the results to our previous work which assumes perfect localization. 

   more » « less