Problem Of Multi-agent Systems Research Articles

Hybrid Event-Triggered Bipartite Consensus Control of Multiagent Systems and Application to Satellite Formation

This work studies event-triggered bipartite consensus problem of multi-agent systems (MAS) with structurally balanced signed graph. A hybrid system approach is proposed to deal with the leaderless and leader-following bipartite consensus problems in a unified framework, and a novel hybrid event-triggering mechanism (ETM) is proposed to reduce the usage of communication resources. By appropriately defining closed-loop states, a unified hybrid model is constructed for both leaderless and leader-following MAS. Then, stability analysis and ETM design results are given under hybrid systems framework. It is worth noting that the designed hybrid ETM is in decentralized form rather than distributed form. Moreover, the lower bound of triggering intervals can be pre-selected, i.e., Zeno-freeness is guaranteed, that is important for control implementation. The proposed method is applied to satellite formation system in simulation results to show the effectiveness. <i>Note to Practitioners</i>&#x2014;This paper considers bipartite consensus control problem of MAS, which can be applied to some practical systems, e.g., bidirectional formation of unmanned air vehicles, since the control objective in these applications can be transformed into bipartite consensus control problem of MAS. Compared with the existing results, a hybrid system approach is proposed for consensus analysis of MAS with or without leader, and a novel event-triggering mechanism is proposed, which is more easier to be implemented, so, the proposed approach is more friendly for control engineers. The obtained results are applied to satellite formation control problem and the effectiveness is verified, and it is expected that the proposed approach can be extended to MAS with more realistic network-induced constraints and applied to more practical engineering systems.

Lag Group Consensus of High-Order Multiagent Systems in Directed Network Settings

This article studies lag group consensus problems of multiagent systems with directed information transformations. Agents in the network are divided into finite groups, and modeled by high-order systems. Distributed consensus protocols with constant lags are presented to realize the lag group consensus: the states of the agents in a group approach to a consensus value asymptotically, while there exist given ratios between the final values of different groups. Necessary and sufficient criteria for the lag group consensus are obtained by the Laplace transformation. Finally, theoretical results are proved to be effective by several simulation results.

Formula omitted] Consensus of multi-agent systems under hybrid cyber attacks via a sampled-data-based dynamic event-triggered resilient consensus protocol

This paper studies the H∞ consensus problem of multi-agent systems (MASs) under external disturbances and hybrid cyber attacks. First, to save limited network resources, a sampled-data-based dynamic event-triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing dynamic event-triggered mechanisms, the negative influences of deception attacks and denial-of-service attacks are considered in the proposed dynamic triggering function. The communication frequencies between agents are reduced by the proposed event-triggered mechanism. Then, based on the proposed sampled-data-based dynamic event-triggered mechanism, a corresponding resilient consensus protocol is designed to ensure that MASs with external disturbances and hybrid cyber attacks can achieve consensus with a weighted H∞ performance. Finally, simulation examples are conducted to verify the effectiveness of the proposed methods.

An RNN-Based Performance Identification Model for Multi-Agent Containment Control Systems

In the containment control problem of multi-agent systems (MASs), the convergence of followers is always a potential threat to the security of system operations. From the perspective of system topology, the inherently non-linear properties of the algebraic connectivity of the follower2follower (F2F) network, combined with the influence of the leader2follower (L2F) topology on the system, make it difficult to design the convergence positions of the followers through mere mathematical analysis. Therefore, in the background of temporary networking tasks for large-scale systems, to achieve the goal of forecasting the performance of the whole system when networking is only completed with local information, this paper investigates the application and effectiveness of recurrent neural networks (RNNs) in the containment control system performance identification, thus improving the efficiency of system networking while ensuring system security. Two types of identification models based on two types of neural networks (NNs), MLP and standard RNN are developed, according to the range of information required for performance identification. Evaluation of the models is carried out by means of the coefficient of determination (R2) as well as the root-mean-square error (RMSE). The results show that each model may produce a better forecasting accuracy than the other models in specific cases, with models based on the standard RNN possessing smaller errors. With the proposed method, model identification can be achieved, but in-depth development of the model in further studies is still necessary to the extent the accuracy of the model.

NNs-observer-based fully distributed consensus control for MASs under deception attacks

This paper proposes a novel resilient intrusion-tolerance control scheme to solve the consensus control problem of multi-agent systems (MASs) under deception attacks. Consider the state unavailability of MASs with unknown nonlinear terms, the unmeasurable state is estimated by designing the neural-networks(NNs)-based observer. Then, a fully distributed intrusion-tolerant adaptive protocol is developed to implement the consensus control task. The designed control strategy only involves the agent states that interact with the neighboring agent and does not relate to the whole communication topology information. The designed controller can ensure that the consensus error of the nonlinear MASs is eventually ultimately bounded under deception attacks. Finally, a numerical simulation is provided to verify the feasibility of the designed scheme.

Distributed Alignment Processes With Samples of Group Average

Reaching agreement despite noise in communication is a fundamental problem in multi-agent systems. Here we study this problem under an idealized model, where it is assumed that agents can sense the general tendency in the system. More specifically, we consider $n$ agents, each being associated with a real-valued number. In each round, each agent receives a noisy measurement of the average value, and then updates its value, which is in turn perturbed by random drift. We assume that both noises in measurements and drift follow Gaussian distributions. What should be the updating policy of agents if their goal is to minimize the expected deviation of the agents' values from the average value? We prove that a distributed weighted-average algorithm optimally minimizes this deviation for each agent, and for any round. Interestingly, this optimality holds even in the centralized setting, where a master agent can gather all the agents' measurements and instruct a move to each agent.We find this result surprising since it can be shown that the total measurements obtained by all agents contain strictly more information about the deviation of Agent $i$ from the average value, than the information contained in the measurements obtained by Agent $i$ alone. Although this information is relevant for Agent $i$, it is not processed by it when running a weighted-average algorithm. Nevertheless, the weighted-average algorithm is optimal, since by running it, other agents manage to fully process this information in a way that perfectly benefits Agent $i$.Finally, we also analyze the drift of the center of mass and show that no distributed algorithm can achieve drift that is as small as the one that can be achieved by the best centralized algorithm. In light of this, we also show that the drift associated with our weighted-average algorithm incurs a relatively small overhead over the best possible drift in the centralized setting.

Bipartite Flocking Control for Multi-Agent Systems With Cooperation-Competition Interactions and Random Packet Dropouts

This paper addresses the bipartite flocking control problem of multi-agent systems with random packet dropouts, in which cooperative and competitive information interactions among agents are described by a signed network. The random packet dropouts are denoted by Bernoulli random variable and the information transmission failure between adjacent agents is possible at every time step. In this case, the unreliable information transmission within multi-agent systems may lead to behavioral dispersion and instability. To solve this problem, a novel fully distributed control scheme is designed. In light of the convergence method for products of infinite sub-stochastic matrices, the bipartite flocking control can be global almost sure to achieve and some essentially algebraic conditions are established. Finally, the effectiveness of the control scheme under random packet dropouts is illustrated by a numerical simulation example.

Resilient Event-Triggered Adaptive Cooperative Fault-Tolerant Tracking Control for Multiagent Systems Under Hybrid Actuator Faults and Communication Constraints

This paper investigates the cooperative fault-tolerant tracking problem for multiagent systems with directed topology that experience communication link faults and actuator faults under malicious attacks. Based on a resilient event-triggered mechanism, a novel distributed high-order sliding-mode observer and a robust adaptive cooperative fault-tolerant control method are proposed, which can achieve the desired formation shape tracking under communication limitations and hybrid actuator faults caused by malicious attacks. First, the designed distributed event-triggered observer can estimate the unknown leader. Second, the proposed event-triggered mechanism can be used to reduce the impact of malicious attacks on the information and physical layers of the multiagent system. Additionally, the utilization rate of network communication is further improved so that it can save communication resources by reducing the number of event triggers. Then, based on the information of the leader, the designed robust adaptive cooperative fault-tolerant controller can compensate for the influence of the hybrid actuator faults on the system under malicious attacks. Finally, the effectiveness of the method is verified by two sets of example simulations.

Quasi-Sure Consensus of Multiagent Systems: G-Noise Communication Protocols Under DoS Attacks

This paper addresses the quasi sure consensus problem for multi-agent systems (MASs) in the context of Denial-of-Service (DoS) attacks. Instead of adopting the conventional noise-based protocols driven by Brownian motion, a novel G-noise communication protocol is first proposed to realize the consensus. Moreover, the consensus protocol not only contains time-varying and nonlinear characteristics, but also focuses on DoS attacks. The protocol driven by G-Brownian motion is characterized in the sublinear expectation framework, which leads to the infeasibility of the existing theory. To overcome the difficulty, a new theoretical framework is established to develop consensus criterion. More specifically, first, the non-zero property of MASs is introduced to ensure the rationality of the construction for the Lyapunov function. Then a sufficient criterion for guaranteeing the consensus of MASs is demonstrated by utilizing Lyapunov method, graph theory, stochastic analysis theory, which depends on DoS duration, noise intensity and G-Brownian motion parameters. Ultimately, theoretical results are associated with islanded microgrid systems with a numerical simulation being exploited.

Preassigned-Time Bipartite Flocking Consensus Problem in Multi-Agent Systems

This article is concerned with the bipartite flocking problem in multi-agent systems. Our contributions can be summarized as follows. Firstly, a class of preassigned-time consensus protocols is proposed to solve the issue of multi-agent systems. Secondly, with the aid of the symmetric properties of the graph theory and the Lyapunov stability theorem, we prove that agents can be divided into two disjointed clusters in a finite time, and they move to opposite directions at the same magnitude and speed. The protocol is novel among existing fixed/finite-time protocols in that the associated settling time is a preassigned constant and a parameter of the protocol. Moreover, it is proven that the diameters of the clusters are bounded and independent of other the protocol parameters. These results are demonstrated through both theoretical analysis and simulation examples.

Prescribed-time containment control of high-order nonlinear multi-agent systems based on distributed observer

This paper investigates the prescribed-time containment control problem for multi-agent systems with high-order nonlinear dynamics under a directed communication topology. Firstly, in view of the fact that only some follower agents can directly access the state information of multiple leader agents, a prescribed-time distributed observer is put forward to estimate the convex hull spanned by these leaders. Then, with the help of the distributed observer, a novel containment control method is developed for each follower based on a time-varying scaling function, so that all followers can converge to the convex hull spanned by the states of multiple leaders within a prescribed time. The comparison with the finite-time and fixed-time control methods differs in that the convergence time of the method proposed in this paper is independent of the initial conditions and control parameters and can be arbitrarily preassigned according to actual needs. Finally, an example is given to demonstrate the usefulness of the prescribed-time containment control method.

Distributed consensus for nonlinear multi-agent systems with two-time-scales: A hybrid reinforcement learning consensus algorithm

The optimal consensus problem for nonlinear two-time-scales multi-agent systems with completely unknown system dynamics is investigated in this paper. First, the original system is linearly represented based on the Takagi-Sugeno fuzzy model. Then, the optimal consensus problem for multi-agent systems is transformed into solving the game algebraic Riccati equation associated with agents and their neighbors. And individual agent dynamics studied in this paper are replaced with local error dynamics. Moreover, an offline hybrid iteration algorithm with rapid convergence speed and no initial stable control policy is presented for multi-agent systems. Meanwhile, to avoid the utilization of the knowledge of system matrices, an online hybrid reinforcement learning algorithm that only uses the state and control input data of each agent and its neighbors is given to generate the distributed optimal control policy. The convergence of proposed algorithms is also discussed. Finally, the applicability of the presented method is illustrated by an example.

Privacy-Preserving Average Consensus in Multiagent Systems via Partial Information Transmission

This article investigates the privacy-preserving average consensus problem in multiagent systems. A new approach is proposed to achieve the accurate average consensus value while protecting the initial state information of agents. The key idea to achieve these goals is based on partial information transmission. Each agent decomposes its initial state information into several different subinformation. The values of these subinformation are chosen randomly but with their mean value fixed to the original initial value. Then, based on multiple communication channels, agents share all their subinformation, but ensure that each neighbor can only obtain partial subinformation. We prove that the privacy can be protected under our method if each agent has at least two neighbors, and one of the neighbors is neutral. The generalization on the directed graph about this method is also considered. Finally, two examples are provided to illustrate the design process and practical applications of the proposed approach.

Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning

We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with its own operator and data observations, cooperatively find the fixed point of the aggregate operator over a decentralized communication graph. Our main contribution is to provide a finite-time analysis of this decentralized stochastic approximation method when the data observed at each agent are sampled from a Markov process; this lack of independence makes the iterates biased and (potentially) unbounded. Under fairly standard assumptions, we show that the convergence rate of the proposed method is essentially the same as if the samples were independent, differing only by a log factor that accounts for the mixing time of the Markov processes. The key idea in our analysis is to introduce a novel Lyapunov-Razumikhin function, motivated by the one used in analyzing the stability of delayed ordinary differential equations. We also discuss applications of the proposed method on a number of interesting learning problems in multi-agent systems.

Leaderless and Leader-Following Bipartite Consensus of Multiagent Systems With Sampled and Delayed Information.

This article proposes a hybrid systems approach to address the sampled-data leaderless and leader-following bipartite consensus problems of multiagent systems (MAS) with communication delays. First, distributed asynchronous sampled-data bipartite consensus protocols are proposed based on estimators. Then, by introducing appropriate intermediate variables and internal auxiliary variables, a unified hybrid model, consisting of flow dynamics and jump dynamics, is constructed to describe the closed-loop dynamics of both leaderless and leader-following MAS. Based on this model, the leaderless and leader-following bipartite consensus is equivalent to stability of a hybrid system, and Lyapunov-based stability results are then developed under hybrid systems framework. With the proposed method, explicit upper bounds of sampling periods and communication delays can be calculated. Finally, simulation examples are given to show the effectiveness.

Dynamic asynchronous edge-based event-triggered consensus of multi-agent systems

This paper studies the asynchronous edge-based dynamic event-triggered consensus problems for multi-agent systems (MASs) with general linear dynamics. First, the static-gain edge-based protocol is proposed to assure that the agents achieve consensus asymptotically and that a uniformly positive minimum inter-event time (MIET) exists under the corresponding dynamic triggering mechanism. Then, to remove the requirement for the global information associated with the graph, an asynchronous edge-based event-triggered consensus control law that involves a dynamic gain is developed. A novel triggering mechanism that includes two dynamic parameters and a class of positive L1 functions under no Zeno behavior is further proposed to determine triggering instants of each edge in the graph. In addition, the continuous communication link is avoided. Finally, the application in a group of fixed-wing aircraft illustrates the validity of the theoretical results.

The most exigent eigenvalue: Guaranteeing consensus under an unknown communication topology and time delays

In the analysis of consensus problems for multi-agent systems affected by time delays, the delay margin for a given protocol is heavily dependent on the communication topology being used. This document aims to answer the question of what is the largest delay value under which convergence to consensus is still possible even if the communication topology is not known. To answer this question we revisit the concept of most exigent eigenvalue, applying it to two different consensus protocols for agents driven by second order dynamics. We show how the delay margin depends on the structure of the consensus protocol and the communication topology, and arrive to a boundary that guarantees consensus for any connected communication topology. The switching topologies case is also studied and It is shown that for one protocol the stability of the individual topologies is sufficient to guarantee consensus in the switching case, whereas for the other one it is not.

Distributed Adaptive Consensus Output Tracking Problem of Nonlinear Multi-Agent Systems with Unknown High-Frequency Gain Signs under Directed Graphs

This paper deals with the consensus output tracking problem for multi-agent systems with unknown high-frequency gain signs, in which the subsystems are connected over directed graphs. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is first presented to tackle adaptive consensus control of network-connected systems without the knowledge of the high-frequency gains. Adaptive laws and internal models are then proposed to handle the uncertainties and unknown parameters. An integral Lyapunov function based on sufficient conditions is finally introduced to tackle the asymmetry of the Laplacian matrix of directed graphs, into which we incorporate the new Nussbaum gain and the adaptive internal model to design the controller. It is apparent that the control scheme and the adaptive laws are fully distributed, which means that only the relative information of the neighbourhood subsystems’ outputs is used, and the simulation results validate the effectiveness of the control design, whereby they guarantee the asymptotic convergence of errors to zero as well as the boundedness of the state variables.

Necessary and sufficient conditions for mean‐square bounded consensus of multiagent systems with additive noise

Motivated from the mean‐square bounded consensus (MSBD) problem of multiagent systems (MASs) with additive noise, this paper studies the joint influence of the agent dynamic structure and communication topology on the consensus. The necessary condition of MSBD is given under the output‐based consensus protocol, which is also sufficient under some mild conditions. Furthermore, the upper consensus bound is given via the solution of the Lyapunov equation. Finally, simulation results are provided to illustrate the theoretical results.

Stochastic convergence problems on switching networks: An event-triggered method

The paper investigates stochastic convergence problems of multi-agent systems with double-integrator dynamics. Based on the consideration of external interference and dynamic topology, a robust controller is proposed to avoid continuous communication between agents via time-dependent event-triggered protocol. The advantage of time-dependent event-triggered protocol is that its effectiveness does not decrease with the increase of the number of agents. Then, in the framework of fixed topology and Markov switching topologies, the convergence conditions are obtained respectively. In addition, singular triggering and Zeno phenomena are excluded. Finally, the effectiveness of the proposed theory is verified by numerical simulations.