Learning-Based Dynamic Connectivity Maintenance for UAV-Assisted D2D Multicast Communication

Unmanned aerial vehicles(UAVs) enable flexible networking functions in emergency scenarios.However,due to the movement characteristic of ground users(GUs),it is challenging to capture the interactions among GUs.Thus,we propose a learningbased dynamic connectivity maintenance architecture to reduce the delay for the UAV-assisted device-todevice(D2D) multicast communication.In this paper,each UAV transmits information to a selected GU,and then other GUs receive the information in a multi-hop manner.To minimize the total delay while ensuring that all GUs receive the information,we decouple it into three subproblems according to the time division on the topology:For the cluster-head selection,we adopt the Whale Optimization Algorithm(WOA) to imitate the hunting behavior of whales by abstracting the UAVs and cluster-heads into whales and preys,respectively;For the D2D multi-hop link establishment,we make the best of social relationships between GUs,and propose a node mapping algorithm based on the balanced spanning tree(BST) with reconfiguration to minimize the number of hops;For the dynamic connectivity maintenance,Restricted Q-learning(RQL) is utilized to learn the optimal multicast timeslot.Finally,the simulation results show that our proposed algorithms perfor better than other benchmark algorithms in the dynamic scenario.

Flocking of multi-robot systems with connectivity maintenance on directed graphs

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.

Distributed Fault-Tolerant Control of Uncertain Multi-Agent Systems with Connectivity Maintenance

In this paper,a distributed cooperative control protocol is presented to deal with actuator failures of multi-agent systems in the presence of connectivity preservation.With the developed strategy,each agent can track the reference trajectory of the leader in the presence of actuator failures,disturbances and uncertainties.The connectivity of the multi-agent system can always be ensured during the control process.To achieve the aforementioned control objectives,a potential function is introduced to the distributed adaptive fault-tolerant control algorithm to preserve the initial connected network among the agents.The uncertainty of the multi-agent system,which is allowed to be described by discontinuous functions,is approximated and compensated using the fuzzy logic system.The asymptotic stability of the closed-loop system is demonstrated through the use of Cellina’s approximate selection theorem of nonsmooth analysis.Due to the developed adaptive laws,the upper bound of the disturbance is allowed to be uncertain,which facilitates the implementation of the control scheme.Finally,simulation results are provided to verify the effectiveness of the proposed control scheme.

Finite-time adaptive formation control for multi-agent systems with uncertainties under collision avoidance and connectivity maintenance

In this paper,the problem of adaptive finite time formation control is investigated for double integrator multi-agent systems with uncertainties.Firstly,considering the multi-agent systems with uncertain dynamic reference and external bounded disturbance,a distributed adaptive estimator control algorithm is designed to realize formation tracking control in finite-time.It is important that the collision avoidance and maintaining connectivity of the multi-agent systems are realized by constructing an effective potential function based on distance constraints.On the other hand,the problem of formation production control for the double integrator systems is discussed with desired formation shape in finite-time.Achieving four control objectives is the main contributions in all the phases,including the estimation of uncertainties,collision avoidance,connectivity maintenance,and finite-time convergence.Finally,an application example of the formation production control is presented to verify accuracy of the proposed theoretical method.