One typical reflection of our understanding on multiagent systems (MASs) is our ability to design the emergence mechanism responsible for their various cooperative behaviors. This paper is concerned with the cooperative robust containment control problem of multileader MASs subject to unknown nonlinear dynamics and external disturbances. Specifically, quasi-containment and asymptotic containment problems are, respectively, considered by using tools from neural network (NN) approximation theory and Lyapunov stability theory of nonsmooth systems. A new kind of containment controllers consisting of a linear local information-based feedback term, a neuro-adaptive approximation term and a nonsmooth feedback term are designed to complete the goal of quasi-containment. Under the assumption that the subgraph depicting the coupling configuration among followers is detail-balanced and each follower can be influenced by at least one leader, it is proven that the containment error vector of the closed-loop MASs will be uniformly ultimately bounded if the control parameters of the proposed containment controllers are suitably designed. By introducing a pseudo ideal weighting matrix for NN approximator embedded at each follower, a novel class of containment controllers are further designed to precisely achieve asymptotic containment in the considered MASs where the Euclidean norm of containment error vector asymptotically converges to zero. At last, numerical simulations are given to verify the validity of these derived theoretical results.
In this paper, a general two-neuron model with time delay is considered, where the time delay is regarded as a parameter. It is found that Hopf bifurcation occurs when this delay passes through a sequence of critical value. By analyzing the characteristic equation and using the frequency domain approach, the existence of Hopf bifurcation is determined. The stability of bifurcating periodic solutions are determined by the harmonic balance approach, Nyquist criterion and the graphic Hopf bifurcation theorem. Numerical results are given to justify the theoretical analysis.
In this paper, the swarming behavior of multiple Euler-Lagrange systems with cooperation-competition interactions is investigated, where the agents can cooperate or compete with each other and the parameters of the systems are uncertain. The distributed stabilization problem is first studied, by introducing an auxiliary system to each agent, where the common assumption that the cooperation-competition network satisfies the digon sign-symmetry condition is removed. Based on the input-output property of the auxiliary system, it is found that distributed stabilization can be achieved provided that the cooperation subnetwork is strongly connected and the parameters of the auxiliary system are chosen appropriately. Furthermore, as an extension, a distributed consensus tracking problem of the considered multiagent systems is discussed, where the concept of equi-competition is introduced and a new pinning control strategy is proposed based on the designed auxiliary system. Finally, illustrative examples are provided to show the effectiveness of the theoretical analysis.
A distributed nonsmooth convex optimization problem subject to a general type of constraint, including equality and inequality as well as bounded constraints, is studied in this paper for a multiagent network with a fixed and connected communication topology. To collectively solve such a complex optimization problem, primal-dual dynamics with projection operation are investigated under optimal conditions. For the nonsmooth convex optimization problem, a framework under the LaSalle's invariance principle from nonsmooth analysis is established, where the asymptotic stability of the primal-dual dynamics at an optimal solution is guaranteed. For the case where inequality and bounded constraints are not involved and the objective function is twice differentiable and strongly convex, the globally exponential convergence of the primal-dual dynamics is established. Finally, two simulations are provided to verify and visualize the theoretical results.
This paper addresses the consensus disturbance rejection problem for multiple-input multiple-output linear multiagent systems (MASs) with directed fixed as well as switching communication topologies in the presence of deterministic disturbances. Based on the relative output information among neighboring agents, a controller, which incorporates the consensus error estimator, the state estimator with static coupling strength and the disturbance observer, is given such that consensus tracking could be achieved in the considered MASs under directed switching communication topologies provided that the control parameters are suitably chosen and the average dwell time is larger than a positive threshold, while the disturbances are fully rejected by the designed disturbance observer. Furthermore, by designing a state estimator with adaptive coupling law, a controller is given such that consensus disturbance rejection of MASs with directed fixed topology could be achieved in a fully distributed manner. The obtained theoretical results are finally validated by performing simulations on the unmanned aerial vehicles.
This brief solves the stabilization problem of linear multiagent systems with discontinuous observations and time-varying parameter uncertainties under an undirected communication topology, where the agents can only intermittently share their information with their neighbors. Distributed observer-type protocols are constructed based on the relative states of neighboring agents and absolute states of a chosen agent. By using Lyapunov methods, some sufficient existence conditions are derived to guarantee the asymptotic stability of the multiagent systems. An algorithm is presented to properly select the coupling strength and feedback gain matrices. Finally, a simulation example is given to verify the theoretical results.
This paper studies the distributed optimization problem with possibly nonidentical local constraints, where its global objective function is composed of $N$ convex functions. The aim is to solve the considered optimization problem in a distributed manner over time-varying unbalanced directed topologies by using only local information and performing only local computations. Towards this end, a new distributed discrete-time algorithm is developed by synthesizing the row stochastic matrices sequence and column stochastic matrices sequence analysis technique. Furthermore, for the developed distributed discrete-time algorithm, its convergence property to the optimal solution as well as its convergence rate are established under some mild assumptions. Numerical simulations are finally presented to verify the theoretical results.
Mobile crowdsensing (MCS) uses participants' computing resources to collect and analyze data and it has been applied in several areas to bring the convenience to people's lives. In MCS, the minimization of travel distance with location privacy is a common objective but should not be the only one practically. Different from the single objective of travel distance minimization, in this paper we formulate a multi-objective optimization model based on bit flipping mechanism, i.e., travel distance minimization and sensing quality score maximization, which is more suitable for a practical scenario. In order to solve the large-scale optimization problem, a Multi-Objective Simulated Annealing approach (MOSA) is utilized to derive a Pareto solution for decision makers. Extensive simulation results illustrate the feasibility and effectiveness of the proposed scheme.
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