This study addresses the problem of fractional-order nonlinear containment control of heterogeneous multi-agent systems within a leader–follower framework, focusing on the impact of False Data Injection (FDI) attacks. By employing adaptive mechanisms and fuzzy logic, the suggested method enhances system resilience, ensuring reliable coordination and stability even in the presence of deceptive disturbances. To deal with these uncertainties, our controller makes use of interval type-II (IT2) fuzzy sets, and we create matrix equalities and inequalities to account for the asymmetry of Laplace matrices. Also, we use the Lyapunov functions for the stability analysis of our system. Lastly, we explain the numerical simulations for the effectiveness of our theoretical results, and these simulated examples are used to verify the effectiveness of our approach and designed model.

The goal of this paper is to mitigate disturbances and input delays while optimizing controller actuation updates for discrete-time multi-agent systems through the use of an event-triggered confinement control system, especially in resource-constrained scenarios. This approach when combined with event-triggered control techniques, then every follower in the system adjusts its condition at specified times based on an event-triggered condition that is suggested. The containment control system issue in the presence of disturbances and input delays was tackled by using both decentralized and centralized event-triggered control systems. Using matrix theory and the Lyapunov technique, convergence analysis is conducted to show that the proposed strategy stays free of zeno phenomena. Numerical boosts are used to further illustrate the impact of theoretical results.

This paper investigates the behavior of fractional-order nonlinear multi-agent systems subjected to Byzantine assaults, specifically focusing on the manipulations of both sensors and actuators. We employ weighted graphs, both directed and undirected, to illustrate the system's topology. Our methodology combines algebraic graph theory with fractional-order Lyapunov techniques to develop algebraic requirements for leader-following consensus, providing a robust framework for analyzing consensus dynamics in these complex systems. We present quantitative results demonstrating the effectiveness of our approach, including two numerical examples that validate the proposed requirements for consensus evaluation. Notably, our work highlights the novelty of using fractional-order systems to enhance resilience against adversarial conditions, contributing significantly to the field of multi-agent systems. By clarifying key terms and streamlining our language, we ensure accessibility for a broader audience while emphasizing the implications of our findings for real-world applications.

This paper addresses the challenges of maintaining stability in heterogeneous vehicle platooning under the influence of communication disruptions caused by Byzantine attacks within a leader-follower framework. To enhance resilience, we propose a nonlinear control strategy tailored for a third-order heterogeneous dynamic model, integrating leader-follower interconnections with adaptable control gains. Utilizing a constant time headway spacing policy with gap adjustments, we derive control gains that secure internal platoon stability. A Lyapunov-based stability approach is employed to mitigate the destabilizing effects of Byzantine attacks, ensuring robust performance even in adverse conditions. Numerical simulations validate the controller's effectiveness, showing rapid convergence to stable platoon behavior within 10 seconds and maintaining a tracking error below 10 percent, despite simulated attacks. These results demonstrate the practical feasibility of the proposed controller in ensuring stability and resilience in real-world platooning scenarios where communication integrity is compromised.

This paper investigates robust cooperative control strategies for multi-robotic car systems operating under sensor and actuator faults. In autonomous driving environments, the degradation or failure of sensors and actuators significantly affects the performance of the system, posing risks to formation control, velocity tracking, and safety. To address these challenges, we propose a robust neural control framework that integrates a dynamic adjustment neural network (DANN) with fault-tolerant design. This architecture enables each robotic car to adaptively learn the system dynamics and adjust control signals in real time, even in the presence of component faults. A fault detection and isolation (FDI) mechanism is incorporated to identify malfunctioning elements, allowing the control system to dynamically compensate and maintain coordinated behavior. Lyapunov-based analysis is employed to guarantee stability and convergence of the system. In addition to theoretical development, a detailed simulation example involving a team of robotic cars under various sensor and actuator fault scenarios is presented to demonstrate the effectiveness and robustness of the proposed control strategy. The results confirm reliable tracking performance, strong resilience, and improved formation stability under realistic fault conditions.

This paper addresses the challenges of achieving robust coordination in discrete-time multi-robot systems subject to uncertainties and Byzantine attacks affecting both actuator and sensor channels. Such adversarial disruptions degrade system performance by corrupting control inputs and state measurements, ultimately threatening stability and consensus in networked robotic systems. To overcome these limitations, a novel discrete-time adaptive control framework is proposed that ensures reliable tracking and stability under both uncoupled and coupled robot dynamics. The approach integrates a modified graph-theoretic structure with node-dependent weighting to capture heterogeneous robot interactions, while explicitly modeling attack effects within the system dynamics. An adaptive control law is developed using a nonlinear basis function approximation to handle unknown system uncertainties, along with a dynamic weight update mechanism that compensates for adversarial disturbances in real time. For the uncoupled case, stability is established through a composite Lyapunov function incorporating logarithmic and quadratic terms, guaranteeing boundedness of all closed-loop signals and asymptotic convergence of the tracking error. This framework is further extended to systems with coupled dynamics by introducing an auxiliary estimation mechanism to reconstruct unmeasurable interactions, leading to a unified adaptive controller capable of mitigating both internal uncertainties and external attacks. Rigorous Lyapunov-based analysis demonstrates that the proposed method ensures asymptotic tracking performance despite the presence of Byzantine disturbances. Numerical simulations validate the theoretical results, showing improved resilience, accurate trajectory tracking, and enhanced robustness compared to existing approaches.