Adaptive Event-Triggered Security Control for Nonlinear CPSs Under Coexisting FDI Attacks and Actuator Faults

Resilient Observer-Triggered Adaptive Control for Cyber-Physical Systems Under Time-Vary Stealthy FDI Attacks

Cyber-physical systems (CPSs) are widely used in safety-critical applications, where both control reliability and communication efficiency are essential. However, open networks make CPSs vulnerable to false data injection (FDI) attacks, which threaten system stability. Existing event-triggered control methods often fail to simultaneously ensure attack resilience, stability, and $H_\infty$ performance. This paper addresses the secure control problem of CPSs under FDI attacks by proposing an observer-based dynamic event-triggered control framework. To counteract the adversarial disturbances, a novel attack-resilient observer is designed to simultaneously estimate both the system states and the injected attack signals, enabling the synthesis of a secure observer-based controller. An advanced dynamic event-triggered mechanism (DETM) is developed by incorporating an internal dynamic variable, which adaptively adjusts triggering thresholds to significantly reduce communication frequency while avoiding Zeno behavior. Through Lyapunov-Razumikhin analysis, the closed-loop system is proven to achieve asymptotic stability and guaranteed $H_\infty$ performance, ensuring robustness against bounded FDI attacks. Theoretical results are validated via numerical simulations, demonstrating the effectiveness of the proposed method in mitigating attack impacts and conserving network resources.

Network-based event-triggered security control for cascade switched systems under hybrid attacks.

Simulations on a steam temperature cascade control system validate the effectiveness of the proposed method, demonstrating 20% faster convergence and a 67% reduction in oscillations compared with a conventional method while maintaining stability and security under hybrid attacks.

Event-Triggered Observer-Based Secure Fault Estimation and Fault-Tolerant Control for Markov Jump Systems

This paper investigates the secure fault estimation (FE) and fault-tolerant control (FTC) problems for Markov jump systems (MJSs) under limited communication resource. First, a dynamic event-triggered mechanism (ETM) is introduced into the sensor-observer channel to alleviate communication burden. Simultaneously, to ensure network security, a class of deception attacks described by Bernoulli random variables is considered during the transmission of sampled outputs. Based on these, a novel dynamic event-triggered intermediate observer (IO) is constructed, which utilizes the sampled outputs corrupted by attack signals to estimate states, faults and disturbances of MJSs. This observer not only reduces data transmission but is also capable of resisting deception attacks. Furthermore, a fault-tolerant controller is designed to maintain system stability. Second, with the aid of augmentation methods, linear matrix inequality techniques and stochastic stability theory, a joint design method for the observer, fault-tolerant controller and dynamic ETM is developed by constructing a model-dependent Lyapunov function that incorporates a dynamic variable. Third, it is proven that the introduced dynamic ETM is free from Zeno behavior. Finally, the effectiveness of the proposed method is validated on an F-404 aircraft engine model. Note to Practitioners—MJSs, as a class of stochastic switching systems, are capable of accurately describing abrupt variations in system structures or parameters that commonly occur in practical engineering scenarios. This capability has enabled their widespread application in critical fields such as aerospace, power and communications. In these fields, frequent equipment faults pose a significant threat to system safety. On the other hand, with the increasing prevalence of networked systems, continuous data transmission imposes heavy communication burdens and increases energy consumption. Meanwhile, data transmitted over networks is vulnerable to cyber attacks. To address these issues, this paper proposes a FTC method based on a dynamic event-triggered observer. Specifically, a dynamic ETM is incorporated into the observer design, which determines whether data should be transmitted according to real-time system states, thereby avoiding unnecessary communication. Moreover, the designed observer is capable of accurately estimating system states, disturbances and faults using measurements corrupted by deception attacks. Finally, the estimated information is integrated into the fault-tolerant controller for online compensation. In summary, this paper provides a practical FTC solution for MJSs subject to communication resource constraints and deception attacks.

Adaptive fuzzy dynamic event-triggered consensus of multi-agent systems under sensor attacks.

As information exchange among agents increases, multi-agent systems with limited communication and energy resources have become increasingly vulnerable to cyber threats, particularly sensor attacks that compromise data integrity and system stability. To address this challenge, this paper proposes a control framework for nonlinear multi-agent systems under sensor attacks, integrating adaptive fuzzy control with dynamic attack detection mechanism and dynamic event-triggered strategies. The proposed detection scheme uses only the local output errors of the agents without requiring knowledge of inter-agent static output mappings, thereby reducing implementation complexity. To achieve consensus tracking under attacks, an adaptive fuzzy consensus controller incorporating Nussbaum-type functions is developed within the backstepping framework to handle uncertain and time-varying output gains caused by attacks. Additionally, a dynamic event-triggered mechanism employing an auxiliary variable is proposed to significantly reduce communication overhead while preserving resilient consensus performance under sensor attacks. Rigorous theoretical analysis proves that all closed-loop signals remain bounded and consensus tracking is achieved despite the presence of attacks. Finally, simulation studies further demonstrate the proposed framework's effectiveness.

Resilient event-triggered consensus control of discrete-time fractional-order multi-agent systems under hybrid cyber attacks

Adaptive Event‐Triggered Mechanism for Uncertain Unmanned Surface Vehicle Systems Under Sequential Scaling Attacks

This investigation focuses on adaptive event triggered fault‐tolerant control for uncertain unmanned surface vehicle systems (UUSVs) in the bearing of actuator failures and sequential scaling attacks. In this model, the effects of actuator failures are addressed through the implementation of fault‐tolerant control strategies. As a result, an adaptive event‐triggered mechanism is proffered to reduce communication and computational overhead while ensuring system stability. In contrast to probabilistic models, a sequential scaling attack is taken into consideration, wherein certain attack characteristics, including attack frequency and duration, are specified. To guarantee stability, a comprehensive set of sufficient conditions has been established using Lyapunov stability theory within the context of linear matrix inequalities (LMIs). At the last, the effectiveness of put forward control scheme is demonstrated through a numerical example.