Silent packet loss (SPL) poses a significant challenge for state estimation, due to the lack of information regarding the packet loss status (PLS). This issue is particularly prominent in event-based systems, where the interplay between event-triggered feedback mechanisms and SPL complicates the estimation process. Existing approaches often employ detectors, but achieving 100% detection accuracy remains nearly impossible, and false detections further complicate the probability distribution of system states. In this article, we propose a silent message-passing mechanism (SMPM) to address the SPL of measurements, which may be coupled with an event-based scheduler, as the feedback channel is also affected by the SPL. Besides, a dynamic Chi-square ( $d$ - $\mathcal {X}^{2}$ ) detector is proposed, whose detection accuracy is proven to converge to 100% with time. Subsequently, an estimator based on the $d$ - $\mathcal {X}^{2}$ detector under the SMPM is designed for unstable systems with the SPL of measurements. More importantly, a stability condition is established, revealing the relationship between the SPL rate and estimation performance. Both simulations and experiments validate the effectiveness of the theoretical findings presented in this article.

This work investigates the oriented-covert attacks and corresponding defense strategies on an unmanned aerial vehicle (UAV) equipped with a GPS sensor and an Ultra-WideBand sensor in single and double base-station scenarios. The attacker drives the UAV away from its nominal path and causes a high-velocity collision while remaining stealthy to base station detection. The attack is formulated as a constrained optimal control problem that trades off terminal deviation and impact velocity, subject to oriented-covert constraints that decompose the control input into detectable and undetectable components. To defend against the above attacks, the defender should enable the UAV to avoid collisions while reaching its nominal destination with minimal energy consumption. Essentially, the attacker–defender interaction is modeled as a Stackelberg game with the defender as the leader and the attacker as the follower. The existence and sensitivity of the game equilibrium are analyzed. Moreover, when the defender adopts the optimal strategy in the sense of a Nash equilibrium, the above game reduces to a unilateral defense optimization problem that aims to compute the optimal control inputs that minimize terminal deviation, impact velocity, and energy consumption simultaneously. Pontryagin’s Maximum Principle is used to derive the optimality conditions and theoretically validate the proposed attack and defense strategies. Finally, the effectiveness and practicality of the proposed attack and defense strategies are demonstrated through two simulation examples and one experiment.

This article investigates state estimation in a smart grid subject to hybrid denial-of-service (DoS) and false data injection (FDI) attacks. The contributions are threefold.Estimator design:An approximate optimal state estimator (AOSE) is designed by merging the exponentially growing Gaussian-mixture into a single Gaussian density. The resulting AOSE balances estimation accuracy and online computational burden, making it implementable for real-world applications.Stealthiness analysis of attacks:Unobservable hybrid DoS and FDI attacks can be exactly detected in some cases. It is proved that the unobservable hybrid attacks are stealthy for a stable smart grid.Stability of AOSE:It is proved that, despite accumulated approximation errors, the AOSE for a smart grid under the stealthy hybrid attacks is stable with a mild initial condition. Finally, some simulations on a three-generator six-bus system validate the performance and stability of the AOSE.

We consider a scenario in which a group of robots tracks a group of targets in an open space. In particular, the robots are heterogeneous, and the targets are adversarial, capable of attacking the robots and severing the links between the robots and their corresponding sensors. Additionally, each robot is required to estimate the state of the targets individually on the basis of the communication graph. We propose a framework that adaptively balances accuracy and safety while automatically repairing the communication graph. Our framework follows a two-stage strategy: In the first stage, we assess the entire team to determine if repair is necessary. If necessary, by quantifying the team’s observability using the trace of the Grammian matrix, we propose a computationally efficient repair strategy. In the second stage, safety and accuracy are quantified, with the sensing margin serving as the dynamic weight to guide robot coordination. To validate the effectiveness of our work, we simulated a monitoring and tracking task and compared our network reconfiguration strategy with greedy and One-Hop-Grammian-based methods. The simulation results demonstrate the effectiveness and efficiency of our approach.

This article considers the finite-frequency (FF) $H_{2}$ - $H_{\infty }$ composite control problem for continuous singularly perturbed systems. To address the performance requirements in the low- and high-frequency ranges, the FF $H_{2}$ and $H_{\infty }$ norms are used to impose on the performance of the slow and fast subsystems, respectively. The FF $H_{2}$ control of the slow subsystem is analyzed using the FF Gramian matrix method. While the FF $H_{\infty }$ control of the fast subsystem is studied by using the Generalized Kalman-Yakubovič-Popov Lemma. Subsequently, an $H_{2}$ - $H_{\infty }$ composite controller for the singularly perturbed system is developed. Finally, two simulation examples involving an armature control direct-current motor system are demonstrated to verify the effectiveness and superiority of the proposed control scheme.

Discovering and tracking wildlife targets are essential for gaining insights into the behavioral patterns and habits of animals within their natural habitats. With low cost and high maneuverability, mini autonomous aerial vehicles (AAVs) can achieve robust and rapid locating and tracking of multiple targets through collaboration. This work proposes a method for multitarget task allocation and path planning for AAV swarms, addressing the challenges of locating and tracking multiple wildlife with very high frequency (VHF) radio tags while avoiding potential disturbances to the wildlife. Our approach proposes a layered framework for the multi-AAV multitarget wildlife tracking problem: 1) the state estimation layer performs fast receiver signal strength indicator (RSSI) signal acquisition and employs the particle filtering algorithm to localize targets’ positions; 2) the task assignment layer uses a quadratic allocation method for AAVs’ real-time target allocation, starting with reasonable initial target sets via mixed-integer programming and efficiently readjusting targets based on real-time environment; and 3) the motion planning layer introduces an optimization-based approach to generate smooth and executable trajectories that can simultaneously ensure desired safe distances from objects of interest. Simulation experiments validate the effectiveness of the obtained AAV swarm tracking scheme.

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve mean-square positive average consensus, and convergence accuracy is evaluated. The proposed mechanism is shown to preserve (ϵ,δ) -differential privacy of MASs, and the privacy budget is derived. Numerical examples are provided to illustrate the effectiveness of the proposed controller and privacy mechanism.

This paper is devoted to designing anH∞fuzzy controller for nonlinear singularly perturbed systems (SPSs) with limited communication bandwidth and parameter uncertainty. The challenges lie in designing a scheduling protocol for slow and fast system dynamics with less conservatism and addressing the issues of mismatched membership functions. The main contributions of this paper are threefold: (1) First, an asynchronous weighted try-once discard (AWTOD) mechanism is designed for slow and fast dynamics to effectively schedule data communication between sensors and the controller. (2) Then, slow and fast controllers are designed using interval type-2 (IT2) fuzzy technique to deal with system nonlinearity and parameter uncertainty. (3) Finally, a composite controller is designed to ensure the asymptotic stability of nonlinear SPSs. The feasibility of our approach is illustrated through a numerical example.

In this article, we focus on the state estimation problems for a system with protecting user privacy. Regarding whether the user has conducted a sensitive action in the system as a kind of privacy, we propose a privacy-preserving mechanism (PPM) to prevent its action results from being disclosed or inferred. For such a system with the PPM, we first obtain the optimal estimator (OE). Subject to the inoperability of the OE in practice, we turn to designing a computationally efficient suboptimal estimator (SE) as an alternative. Then, we prove that this SE can remain stable while satisfying the user's requirements on both privacy protection and estimation performance. By solving a privacy-preserving optimization problem, a set of guidelines is established to customize a tradeoff between privacy and performance according to the user's demand. Finally, illustrated examples are used to illustrate the main theoretical results.

Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This article investigates a class of multivariable discrete-time multiagent systems with ϵ -differential privacy preserved. A novel information-masking mechanism is proposed, in which the information of each state transmitted to different neighbors is obscured by adding independent random noises. Then, the mean-square consensus conditions, and the upper bound and lower bound of the convergence rate are obtained. Moreover, the conditions for the convergence rate reaching its upper bound are established. The results can be applied to the average mean-square consensus. In addition, a necessary and sufficient condition is presented under which agents can preserve the dynamics of agents ϵ -differentially private at any time instant.

In this paper, we study the state estimation and optimal control [i.e., linear quadratic Gaussian (LQG) control] problems for networked control systems in which control inputs, observations, and packet acknowledgments (ACKs) are randomly lost. The packet ACK is a signal that is transmitted from the actuator to notice the estimator the occurence of control packet loss. For such systems, we obtain the optimal estimator, which is consisted of exponentially increasing terms. For the solvability of the LQG problem, we come to a conclusion that in general even the optimal LQG control exists, it is impossible and unnecessary to be obtained as its calculation is not only technically difficult but also computationally prohibitive. This issue motivates us to design a suboptimal LQG controller for the underlying systems. We first develop a suboptimal estimator by using the estimator gain in each term of the optimal estimator. Then we derive a suboptimal LQG controller and establish the conditions for stability of the closed-loop systems. Examples are given to illustrate the effectiveness and advantages of the proposed design scheme.