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In this paper, the problem about the false data injection attacks on sensors to degrade the state estimation performance in cyber-physical systems(CPSs) is investigated. The attack strategies for unstable systems and stable ones are both designed. For unstable systems, based on the idea of zero dynamics, an unbounded attack strategy is proposed which can drive the state estimation error variations to infinity. The proposed method is more general than existing unbounded attack strategies since it relaxes the requirement for the initial value of the estimation error. For stable systems, it is difficult to bring unbounded impacts on the estimation error variations. Therefore, in this case, an attack strategy with adjustable attack performance which makes the estimation error variations track predesigned target values is proposed. Furthermore, a uniform attack strategy which aims to deteriorate state estimation for both stable systems and unstable ones is derived. Finally, simulations are provided to illustrate the effectiveness of the proposed attack strategies. [ABSTRACT FROM AUTHOR]

To solve the poor performance of reinforcement learning (RL) in the multi-agent flocking cooperative control under the communication-restricted environments, we propose a multi-agent cooperative RL (MACRL) method based on the equivalent characteristics of the agents in the flocking task. A distance graph attention (GAT) mechanism is introduced into the policy network of the proposed MACRL to change the agent's attention weights related to neighbors and reduce the influence of remote neighbors which have poor communication quality on the agent's behavioral decision-making. Furthermore, a distance GAT-based MACRL (DGAT-MACRL) algorithm is proposed for multi-agent flocking control in the communication-restricted environment. The simulation results show that the proposed flocking algorithm has good adaptability to the communication delay and the communication distance constraint environments. The flocking control effect is significantly better than other RL-based flocking algorithms and traditional flocking algorithms. In addition, the good experimental performance has also confirmed that the proposed DGAT-MACRL is an effective solution to the problem of how to improve the adaptability of traditional RL to the flocking control system with a dynamic scale. Our algorithm provides a novel and practical algorithm offering an effective method for the cooperative task accomplished by multi-agent in non-ideal environments. [ABSTRACT FROM AUTHOR]

The force method and displacement method on the basis of the reciprocal theorem play an important role in the field of structural mechanics and have been successfully applied in structural mechanics. However, it is interestingly found that the unexpected paradox exists when the authors attempt to apply it to problems of deformations of strain gradient beams. The reciprocal relation between higher order stresses and higher order strains within the framework of linear elastic strain gradient elasticity is proposed with a view toward studying the physical nature of this paradoxical phenomenon, and it is then used to prove the updated reciprocal theorem. At the same time, the reciprocal theorem of any gradients of any second-order symmetric stress tensors and their corresponding gradients of displacements are derived according to the proposed reciprocal relation. The results show that the essential reason for the failure of the conventional reciprocal theorem is that the effect of higher order surface forces and surface stresses that are produced by strain gradients contributes to the reciprocal work. When the strain gradients work-conjugating to stress gradients are considered, they satisfy the local reciprocal relation that cannot be degenerated to the conventional reciprocal theorem in the form of body forces and inertial forces. The theory developed in this paper may have an increasingly profound effect on continuum mechanics and is expected to be a helpful tool for the mechanics of cellular structures homogenized by strain gradient elasticity. • Conventional reciprocal relation breaks down within the framework of strain gradient elasticity. • We propose new reciprocal theorem accounting for the contribution of surface body force. • Physical explanations of new reciprocal theorem are graphically presented. [ABSTRACT FROM AUTHOR]

This paper is concerned with the security problem of distributed state estimation for stochastic multi-agent systems (MASs) under directed topology. First, in the absence of attacks, an optimal distributed filter is proposed to reconstruct the states of MASs by minimizing the mean square error, which makes full use of the measurable local output information and relative state estimates of agents. Moreover, instead of using a single detection mechanism to measure the stealthiness of attacks, based on the structure of the derived filter, a composite attack detection mechanism capable of simultaneously detecting anomalies in the sensor channels of the agents themselves and the network topologies between different agents is utilized in this paper. Then, from the perspective of potential adversaries, two stealthy attack strategies are established through a joint design of sensor and topology attacks, enabling attackers to fulfill diverse attack objectives while evading the given composite detection mechanisms. Finally, simulation results are provided to verify the availability of the designed filter and attack generation schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of complete stealthiness false data injection (FDI) attacks in cyber-physical systems with Kullback-Leiber divergence (KLD) detector. Different from the FDI attacks against KLD detector in the related researches, the FDI attacks with complete stealthiness can completely remove their influences on innovation. Especially, we present the sufficient and necessary design conditions for achieving the attacks. In addition, the realization method of data-driven attacks based on subspace identification is introduced when the attacker cannot acquire system parameters. And a DC motor system and an unmanned ground vehicle system are employed to verify the effectiveness of our main results. [ABSTRACT FROM AUTHOR]

Flocking control is one of the important topics in multi-agent system (MAS), and has great value in both military and civilian applications. At present, the slow cluster consensus and poor control strategy in the stochastic environment are still serious problems faced by the flocking control algorithms. Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking control system. In HGNN-MADDPG, we design a GNN with loss weight to complete the feature extraction of the structural information and improve the perception ability of agents to the system structure. Furthermore, in order to improve training efficiency, we also propose a new cooperative reinforcement learning architecture in which the network and experience between agents are shared. Simulation results show that the HGNN-MADDPG has a better learning effect and faster convergence than the traditional RL algorithms. In addition, the comparative experiments demonstrate the leader-follower flocking algorithm based on HGNN-MADDPG has a faster cluster consensus and more stable control than the traditional and other RL-based leader-follower algorithms in the stochastic environment. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of strictly stealthy attacks design against nonlinear multi-agent systems (MASs) with directed communication topology. First, a distributed attack detection mechanism with powerful detection capability is used to detect system anomalies and measure the concealment of attacks. Then, under different attack scenarios, the necessary and sufficient design conditions of strictly stealthy attacks which can bypass the distributed detection mechanism and lead to MASs insecurity are derived. Moreover, by combining the basic idea of blocking attacks and false data injection attacks (FDIAs), a strictly bounded stealthy attack scheme is proposed, which considers the energy constraint of attackers. Finally, simulation examples are carried out to show the effectiveness and superiority of the developed stealthy attack strategies. [ABSTRACT FROM AUTHOR]