Connectivity Of Communication Graph Research Articles

Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-unmanned aerial vehicle (UAV) systems. The multi-UAV decision-making problem is modeled in the framework of distributed multi-agent partially observable Markov decision processes (MPOMDPs). Specifically, the state of the targets is represented by the joint multi-target probability distribution (JMTPD), which is estimated by a distributed information fusion strategy. In the information fusion process, the most accurate estimation is selected to propagate through the whole network in finite time. We propose a max-consensus protocol to guarantee the consistency of the JMTPD. It is proven that the max-consensus can be achieved in the connected communication graph after a limited number of iterations. Based on the consistent JMTPD, the distributed partially observable Markov decision algorithm is used to make tracking decisions. The proposed method uses the Fisher information to bid for targets in a distributed auction. The bid is based upon the reward value of the individual UAV’s POMDPs, thereby removing the need to optimize the global reward in the MPOMDPs. Finally, the cooperative decision-making approach is deployed in a simulation of a multi-target tracking problem. We compare our proposed algorithm with the centralized method and the greedy approach. The simulation results show that the proposed distributed method has a similar performance to the centralized method, and outperforms the greedy approach.

Adaptive cooperative control of networked uncalibrated robotic systems with time‐varying communicating delays

This paper investigates the trajectory tracking control of the networked multimanipulator with the existence of time‐varying delays and uncertainties in both kinematics and dynamics. To address time‐varying delays in the communication links, a novel control scheme is established by the design of delay–rate‐dependent networking mutual coupling strengths. Besides, to handle the kinematic and dynamic uncertainties, an adaptive controller is designed. The proposed control scheme guarantees that the networked robotic system can track a commonly desired trajectory cooperatively with the strongly connected communication graph, uncertainties, and time‐varying communicating delays. A Lyapunov–Krasovskii functional is employed to rigorously prove the asymptotic convergence of both tracking errors and synchronization errors. The simulation results are provided to verify the effectiveness of the control method proposed by this paper.

Second-order min-consensus on switching topology

The agent dynamics of most of the multi-agent systems in practice are second-order. However, there is no existing min-consensus protocol for such systems. In this paper, we deal with this problem by proposing the first second-order min-consensus protocol with provable convergence. It is not trivial to extend the min-consensus result for the first-order case to the second-order one. Under certain conditions, the proposed protocol can guarantee global asymptotic min-consensus, even for the case with jointly connected communication graphs. An illustrative example is presented to verify the theoretical results and the efficiency of the proposed protocol.

Fully Distributed Fault-Tolerant Consensus Protocols for Lipschitz Nonlinear Multi-Agent Systems

In this paper, we investigate the fault-tolerant consensus problems for multi-agent systems with actuator faults. Based on the relative state information, fully distributed adaptive protocols are designed for these agents described by Lipschitz nonlinear dynamics under leaderless and leader-follower communication structures. As the special case, for linear multi-agent systems, fault-tolerant consensus problems are also solved with the proposed protocols. First, for the leaderless multi-agent systems, all agents reach an agreement on a common value with undirected connected communication graphs. Second, for the leader-follower multi-agent systems, in which the state of the leader is only available to a subset of the followers, the state of each follower converges to the state of the leader, asymptotically. A distinctive feature of this paper is that the adaptive protocols here are independent of the eigenvalues of the Laplacian matrix associated with the whole communication graph, which means the protocols can be implemented by each agent in a fully distributed fashion. Simulation examples are provided to illustrate the theoretical results.

Antenna Orientation and Range Assignment Algorithms in Directional WSNs

Consider a set S of nodes in the plane such that the unit-disk graph G(S) spanning all nodes is connected. Each node in S is equipped with a directional antenna with beam-width θ = π/2. The objective of the directional antenna orientation (AO) problem concerning symmetric connectivity is to determine an orientation of the antennas with a minimum transmission power range r = O(1) such that the induced symmetric communication graph is connected. Another related problem is the AO and power assignment (AOPA) problem whose objective is to assign each node u ∈ S an orientation of its antenna as well as a range r(u) such that the induced symmetric communication graph is connected and the total power assigned Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u∈S</sub> r(u) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sup> is minimized, where β ≥ 1 is the distance-power gradient (typically 2 ≤ β ≤ 5). In this paper, we study both problems by first proving that they are NP-hard. We then propose two algorithms for the AO problem that orient the antennas to yield a symmetric connected communication graph where the transmission power ranges are bounded by 9 and 7, respectively, which are currently the best results for this problem. We also propose constant-factor approximation algorithms for the AOPA problem where our constants are smaller than Aschner et al's. Finally, we study the performance of our algorithms through simulations.

Distributed Nash Equilibrium Seeking by a Consensus Based Approach

In this paper, Nash equilibrium seeking among a network of players is considered. Different from many existing works on Nash equilibrium seeking in non-cooperative games, the players considered in this paper cannot directly observe the actions of the players who are not their neighbors. Instead, the players are supposed to be capable of communicating with each other via an undirected and connected communication graph. By a synthesis of a leader-following consensus protocol and the gradient play, a distributed Nash equilibrium seeking strategy is proposed for the non-cooperative games. Analytical analysis on the convergence of the players' actions to the Nash equilibrium is conducted via Lyapunov stability analysis. For games with non-quadratic payoffs, where multiple isolated Nash equilibria may coexist in the game, a local convergence result is derived under certain conditions. Then, a stronger condition is provided to derive a non-local convergence result for the non-quadratic games. For quadratic games, it is shown that the proposed seeking strategy enables the players' actions to converge to the Nash equilibrium globally under the given conditions. Numerical examples are provided to verify the effectiveness of the proposed seeking strategy.

Consensus of Fractional Multi‐Agent Systems Using Distributed Adaptive Protocols

AbstractThis paper is concerned with the adaptive consensus problem of fractional multi‐agent systems for both the linear and nonlinear cases. Distributed adaptive protocols are designed, respectively, for linear and nonlinear fractional multi‐agent systems, under which consensus is achieved for any undirected connected communication graph without using any global information. Furthermore, the leader‐following problem is studied as an extension. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results.

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

ABSTRACTThis paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Establishing symmetric connectivity in directional wireless sensor networks equipped with $$2\pi /3$$ 2 π / 3 antennas

In this paper, we study the antenna orientation problem concerning symmetric connectivity in directional wireless sensor networks. We are given a set of nodes each of which is equipped with one directional antenna with beam-width $$\theta = 2\pi /3$$ and is initially assigned a transmission range 1 that yields a connected unit disk graph spanning all nodes. The objective of the problem is to compute an orientation of the antennas and to find a minimum transmission power range $$r=O(1)$$ such that the induced symmetric communication graph is connected. We propose two algorithms that orient the antennas to yield symmetric connected communication graphs where the transmission power ranges are bounded by 6 and 5, which are currently the best results for this problem. We also study the performance of our algorithms through simulations.

Distributed Adaptive Consensus Protocol with Decaying Gains on Directed Graphs

Abstract: In this paper we present a distributed adaptive consensus protocol, that solves the cooperative regulator problem for multi-agent systems with general linear time-invariant dynamics and directed, strongly connected communication graphs. The protocol addresses the problems of recent distributed adaptive consensus protocols with large or unbounded coupling gains. These problems are solved by introducing a novel coupling gain dynamics that allows the coupling gains to synchronize and decay to some estimated value. Unlike the static consensus protocols, which require the knowledge of the smallest real part of the non-zero Laplacian eigenvalues to design the coupling gain, the proposed adaptive consensus protocol does not require any centralized information. It can be therefore implemented on agents in a fully distributed fashion.

Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback

This paper addresses the cooperative path-following problem of multiple marine surface vehicles subject to dynamical uncertainties and ocean disturbances induced by unknown wind, wave and ocean current. The control design falls neatly into two parts. One is to steer individual marine surface vehicle to track a predefined path and the other is to synchronise the along-path speed and path variables under the constraints of an underlying communication network. Within these two formulations, a robust adaptive path-following controller is first designed for individual vehicles based on backstepping and neural network techniques. Then, a decentralised synchronisation control law is derived by means of consensus on along-path speed and path variables based on graph theory. The distinct feature of this design lies in that synchronised path following can be reached for any undirected connected communication graphs without accurate knowledge of the model. This result is further extended to the output feedback case, where an observer-based cooperative path-following controller is developed without measuring the velocity of each vehicle. For both designs, rigorous theoretical analysis demonstrate that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy.

Coordination control of multi-agent systems with second-order nonlinear dynamics using fully distributed adaptive iterative learning

This paper investigates the distributed coordination problem for leader–follower multi-agent systems with second-order nonlinear dynamics by using adaptive iterative learning control. It is assumed that the state information of the leader is only available to a portion of the follower agents and the bounded input of the leader is unavailable to any follower agent. Without using any global information, a fully distributed adaptive iterative learning protocol with distributed adaptive learning laws for the control gains is designed in this paper, under which consensus is reached for all undirected connected communication graph. Furthermore, as an extension of the former result, the formation problem is studied. Finally, simulation examples are given to illustrate the theoretical analysis.

Robust synchronisation of networked Lagrangian systems and its applications to multi‐robot teleoperation

This study studies robust synchronisation for networked Lagrangian systems under dynamic uncertainties and communication delays. Using weighted storage functions, the authors propose a robust control scheme to guarantee that networked Lagrangian systems achieve ultimately bounded solutions over strongly connected communication graphs. Since the developed control system can ensure synchronous action in the absence of a common trajectory, the robust control algorithm is applied for bilateral teleoperations with multiple remote robots. The ultimate boundedness of the solutions can enable the teleoperation system to account for the presence of uncertain dynamics when a human operator intermittently intervenes on local robots. Numerical examples of a group of two-link non-linear robotic systems are presented to validate the efficacy of the developed control algorithms.

Consensus of nonlinear multi‐agent systems with adaptive protocols

This study is concerned with the problem of dynamical distributed consensus for multi-agent systems with nonlinear dynamics. Following the nearest neighbour rule, an adaptive consensus protocol is designed for such systems without using any global information, where the coupling weight of an agent from its neighbours adaptively updates according to the differences from the mean activity of the agent and its neighbours. The analysis shows that, under some mild assumptions, the adaptive law can achieve local and global consensus for any network with connected communication graph. Numerical simulations, illustrated by a common second-order consensus example, are performed to demonstrate the effectiveness of the presented results.

Consensus of multi‐agent systems with linear dynamics using event‐triggered control

This study studies the consensus problem of linear multi-agent systems via event-triggered control. Two novel event-triggered control schemes, one centralised and the other distributed, are developed. It is shown that under the proposed control protocols, consensus can be reached for all undirected connected communication graphs. An example is finally presented to illustrate the effectiveness of the proposed control methods.

Adaptive Leader-Following Consensus of Multi-Agent Systems with Unknown Nonlinear Dynamics

This paper deals with the leader-following consensus of multi-agent systems with matched nonlinear dynamics. Compared with previous works, the major difficulty here is caused by the simultaneous existence of nonidentical agent dynamics and unknown system parameters, which are more practical in real-world applications. To tackle this difficulty, a distributed adaptive control law for each follower is proposed based on algebraic graph theory and algebraic Riccati equation. By a Lyapunov function method, we show that the designed control law guarantees that each follower asymptotically converges to the leader under connected communication graphs. A simulation example demonstrates the effectiveness of the proposed scheme.

Adaptive motion coordination of passive systems under quantization effect

Summary In this paper, we consider the quantization effect on an adaptive motion coordination control law for passive multi-agent systems when the time-varying reference velocity is only available to the leader. Both logarithmic quantizers and uniform quantizers are investigated. Using tools from nonsmooth analysis and the Barbalat lemma, we prove that with undirected connected communication graphs, coordination problem is still solvable when logarithmic quantizers are used and the parameter convergence is also guaranteed when the basis functions satisfy certain persistently exciting property. When uniform quantizers are used, we prove that practical consensus can be achieved, and both the bounds of the consensus error and the velocity tracking error are proportional to the parameter of the quantizer. Simulation examples are provided to illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.

Characteristics of swarms on the edge of fragmentation.

Fragmentation of particle swarms into isolated subgroups occurs when interaction forces are weak or restricted. In the restricted case, the swarm experiences the onset of bottlenecks in the graph of interactions that can lead to the fragmentation of the system into subgroups. This work investigates the characteristics of such bottlenecks when the number of particles in the swarm increases. It is shown that certain characteristics of the bottleneck can be captured by considering only the number of particles in the swarm. Considering the case of a connected communication graph constructed in the hypothesis that each particle is influenced by a fixed number of neighboring particles, a limit case is determined for which a lower limit to the Cheeger constant can be derived analytically without the need for extensive algebraic calculations. Results show that as the number of particles increases, the Cheeger constant decreases. Although ensuring a minimum number of interactions per particle is sufficient, in theory, to ensure cohesion, the swarm may face fragmentation as more particles are added to the swarm.

Eigenvalue Based Approach for Global Consensus in Multiagent Systems with Nonlinear Dynamics

This paper addresses the global consensus of nonlinear multiagent systems with asymmetrically coupled identical agents. By employing a Lyapunov function and graph theory, a sufficient condition is presented for the global exponential consensus of the multiagent system. The analytical result shows that, for a weakly connected communication graph, the algebraic connectivity of a redefined symmetric matrix associated with the directed graph is used to evaluate the global consensus of the multiagent system with nonlinear dynamics under the common linear consensus protocol. The presented condition is quite simple and easily verified, which can be effectively used to design consensus protocols of various weighted and directed communications. A numerical simulation is also given to show the effectiveness of the analytical result.

Distributed Informative-Sensor Identification via Sparsity-Aware Matrix Decomposition

Covariance matrices that consist of sparse factors arise in settings where the field sensed by a sensor network is formed by localized sources. It is established that the task of identifying source-informative sensors boils down to estimating the support of the sparse covariance factors. To this end, a novel sparsity-aware matrix decomposition framework is developed that can recover the support of the sparse factors of a matrix. Relying on norm-one regularization a centralized formulation is first derived, when sensors are fully connected. Then, the notion of missing covariance entries is employed to develop a distributed sparsity-aware matrix decomposition scheme. The associated minimization problems are solved using computationally efficient coordinate descent iterations combined with matrix deflation mechanisms. A simple scheme is also developed to set appropriately the sparsity-adjusting coefficients. The distributed framework can provably recover the support of the covariance factors when field sources do not overlap, while each subset of sensors sensing a specific source form a connected communication graph. Different from existing approaches, the novel utilization of covariance sparsity allows distributed informative sensor identification, without requiring data model parameters. Numerical tests corroborate that the novel factorization schemes work well even when the sources overlap.