Realization of consensus of multi-agent systems with stochastically mixed interactions

Multiplicative measurement noise can facilitate consensus of multiagent networks

Measurement noise may have an important impact on the collective motion. Here, we investigate the consensus problem of multiagent networks with multiplicative measurement noise. Based on the stability theory of stochastic differential equations and the algebra graph theory, we obtain sufficient conditions for the consensus and nonconsensus. Both of our analytical and numerical results show that the multiplicative measurement noise can facilitate the emergence of the consensus: the convergence rate increases with respect to the noise intensity if the topologies of the underlying networks satisfy some conditions. Our results provide a better understanding of the constructive role of noise. We also report that the convergence rate of multiagent networks is strongly affected by the network topology and the group size.

Temnothorax albipennis migration inspired semi-flocking control for mobile sensor networks

Mobile sensor networks (MSNs) are utilized in many sensing applications that require both target seeking and tracking capabilities. Dynamics of mobile agents and the interactions among them introduce new challenges in designing robust cooperative control mechanisms. In this paper, a distributed semiflocking algorithm inspired by Temnothorax albipennis migration model is proposed to address the above issues. Mobile agents under the control of the proposed semiflocking algorithm are capable of detecting targets faster and tracking them with lower energy consumption when compared with existing MSN motion control algorithms. Furthermore, the proposed semiflocking algorithm can operate energy-efficiently on both flat and uneven terrains. Simulation results demonstrate that the proposed semiflocking algorithm can provide promising performances in target seeking and tracking applications of MSNs.

A distributed cooperative approach for unmanned aerial vehicle flocking

This paper studied the fixed-wing unmanned aerial vehicle (UAV) flocking problem from the biological point of view on the basis of the UAV model governed by the complete 12 variables. A weighted and undirected graph is applied to describe the time-variant metric interaction relationship among fixed-wing UAVs. Based on the proposed model and the communication mechanism, a distributed cooperation approach is designed to force groups of fixed-wing UAVs to collaboratively accomplish predefined tasks such as imitating a flock of birds. During the evolution process, four constraint conditions should be considered. The first one is that each UAV flies under bounded state variables, including attitude angle, velocity, and attitude angle speed. Second, one forward speed is necessary for the flight of each fixed-wing UAV. The third one concerns the aviation safety problem. Considering the real size of the fixed-wing UAV, the minimal distance between any two UAVs during the evolution process should be large enough to avoid collisions. The last constraint condition is that the lesser the adjustment time, the more likely it will be within a steady-state error margin. Four constraint conditions are skillfully taken as evaluation criteria to determine the coupling strength of the communication network. Numerical simulations are provided to validate the feasibility of the proposed approach for fixed-wing UAVs to implement the steady flight task of variable-altitude flocking and the steady flight task of invariable-altitude flocking.

Consensus in averager-copier-voter networks of moving dynamical agents

This paper deals with a hybrid opinion dynamics comprising averager, copier, and voter agents, which ramble as random walkers on a spatial network. Agents exchange information following some deterministic and stochastic protocols if they reside at the same site in the same time. Based on stochastic stability of Markov chains, sufficient conditions guaranteeing consensus in the sense of almost sure convergence have been obtained. The ultimate consensus state is identified in the form of an ergodicity result. Simulation studies are performed to validate the effectiveness and availability of our theoretical results. The existence/non-existence of voters and the proportion of them are unveiled to play key roles during the consensus-reaching process.