Search

This paper is concerned with the secure bipartite consensus of second-order multi-agent systems under denial-of-service (DoS) attacks. The communication network is an antagonistic network, in which there is cooperative or competitive relationship between neighboring agents. Meanwhile, information cannot be transmitted when the system is attacked. A novel event-triggered control algorithm based on sampled data is proposed to save limited resources and exclude the Zeno behavior. By applying the convergence of monotone sequences, graph theory as well as the discrete-time Lyapunov function method, some sufficient conditions on threshold parameters, frequency and duration of DoS attacks, and sampling period are derived to ensure the bipartite consensus under DoS attacks. Finally, the correctness and advantages of theoretical results are demonstrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper studies practical consensus problems for continuous-time multiagent systems with heterophilous dynamics. Agents in the network tend to cease communication when they are close enough to each other inspired by the heterophily principle in social and opinion dynamics. We propose two types of state constraints on dynamical agents, in which the hard constraint refers to exogenous restrictions that rule the entire evolution of the agent behavior and the soft constraint turns out to be endogenous and only the ultimate equilibria of the agents are restricted. Sufficient conditions have been established to achieve practical consensus for both types of constrained systems. Numerical simulations show that the practical consensus time grows logarithmically, revealing a rapid convergence with respect to disagreement of initial conditions. [ABSTRACT FROM AUTHOR]

In this paper, the problem of localizing and circumnavigating an unknown stationary target by multiple autonomous agents using bearing-only measurements is studied. We assume that all agents are anonymous in the sense that they are indistinguishable from each other, and each agent is not permitted to exchange its local measurements or state information. A novel collision avoidance mechanism is proposed to ensure collision-free system evolution, and a distributed control algorithm is developed to steer the group of agents to circumnavigate the target at a desired distance with any prescribed angular spaced formation. Global finite-time stability of the system is rigorously derived. The performance of the proposed algorithms is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

In most existing studies, formation tracking algorithms are generally based on state feedback strategies and assume that agents are treated as points. However, in practical scenarios, directly obtaining the state of the target can often be challenging, and agents may have their own shapes. In this paper, we model agents as rectangles, allowing each agent to establish a safety zone to avoid collisions during formation. Based on these rectangular agents, we design formation tracking algorithms for a mobile target using only local output information. Specifically, two layers of control algorithms are designed for each agent. (1) Upper-level algorithm: two types of fully distributed mobile observers are constructed based on only a portion of the target's output information. This is achieved by applying node-based adaptive coupling gains and edge-based adaptive gains to the consensus-based term in the state estimation algorithm, respectively. (2) Lower-level algorithm: a leader-following formation tracking algorithm is constructed. By cascading these two layers of algorithms, all agents track the target in formation at the same speed as the target. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with the privacy-preserving average consensus problem for continuous-time multi-agent network systems (MANSs) based on the event-triggered strategy. A novel event-triggered privacy-preserving consensus algorithm is designed to achieve the average consensus of MANSs while avoiding the disclosure of the agents' initial states. Different from the approaches incorporating stochastic noises, an output mask function in the proposed algorithm is developed to make initial state of each agent indiscernible by the others. Particularly, under the output mask function, all agents can exactly tend to the average value of initial states rather than the mean square value. Under the proposed algorithm, detailed theoretical proof about average consensus and privacy of the MANSs are conducted. Moreover, the proposed algorithm is extended to nonlinear continuous-time MANSs, and the corresponding results are also derived. A numerical simulation eventually is performed to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]