Adaptive Event-Triggered Distributed Estimation for a Class of Non-Linear Systems over Sensor Networks with Replay Attacks: The Finite-Horizon Case

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.

Distributed Adaptive Probabilistic Event‐Triggered Filtering for Fuzzy Systems Under Data‐Important‐Aware DoS Attack

This study proposes a novel distributed filtering framework for discrete‐time nonlinear systems over sensor networks, tackling challenges from bandwidth limitations, communication delays, and cyber‐attacks. An adaptive probabilistic event‐triggered mechanism is introduced, utilizing a dynamic threshold with probabilistic delay division to reduce transmissions and handle stochastic delays. This is combined with a dynamic saturation function featuring time‐varying adaptively bounds to suppress outliers and cyber‐attacks. A resilient distributed Takagi–Sugeno (T–S) fuzzy filtering strategy is then developed based on piecewise Lyapunov functionals and linear matrix inequalities, ensuring stochastic stability and prescribed performance. Additionally, a data‐importance‐aware denial‐of‐service (DoS) attack model is proposed, where attackers target high‐value packets via a weighted error metric to maximize impact efficiently. Simulations confirm that the framework improved estimation accuracy, lowers communication rates, and strengthens resilience against targeted attacks compared to existing methods. The integrated design offers a theoretically guaranteed and practically effective solution for secure distributed filtering in cyber‐physical systems.

Resilient State and Fault Estimation for Stochastic Nonlinear Systems Under Dynamic Event-Triggered Schemes

This paper focuses on the joint non-fragile state and fault estimation issue for a class of stochastic nonlinear systems under the dynamic event-triggered transmission scheme (DETS). To better conform to practical engineering scenarios, we consider an additive fault whose second-order difference is piecewise zero. A zero-mean matrix with bounded covariance is adopted to characterize the phenomenon of random gain variation. The threshold parameter of the DETS is adjustable via a given dynamic equation, rather than being fixed. By extending the original state with the fault and its first-order difference, the original system is converted into a stochastic parameter one. Accordingly, the goal of this paper is to design a non-fragile filter, which ensures an upper bound (UB) for the filtering error covariance (FEC) represented by specific matrix difference equations; thereafter, the gain parameter is determined by minimizing the acquired UB. Subsequently, a sufficient condition is derived regarding the mean-square boundedness of the filtering error. Finally, a numerical example is given to confirm the effectiveness of our estimation algorithm.

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

Resilient prescribed-time consensus for input-saturated multi-agent systems under stealthy FDI and DoS attacks: a dynamic watermarking and DPTUIO Approach