Average Consensus Of Multi-agent Systems Research Articles

Differentially Private Average Consensus of Multi-Agent Systems via Privacy-Based Dynamic Self-Triggered Mechanism

Differentially Private Average Consensus of Multi-Agent Systems via Privacy-Based Dynamic Self-Triggered Mechanism

Aperiodically Intermittent Event-Triggered Optimal Average Consensus for Nonlinear Multi-Agent Systems.

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

Improved Fixed-Time Event-Triggered Average Consensus of Multi-Agent Systems Under DoS Attacks

Improved Fixed-Time Event-Triggered Average Consensus of Multi-Agent Systems Under DoS Attacks

Differentially Private Average Consensus for Networks With Positive Agents.

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve mean-square positive average consensus, and convergence accuracy is evaluated. The proposed mechanism is shown to preserve (ϵ,δ) -differential privacy of MASs, and the privacy budget is derived. Numerical examples are provided to illustrate the effectiveness of the proposed controller and privacy mechanism.

Convergence speed of dynamic consensus with delay compensation

A well-known drawback in distributed average consensus of multi-agent systems is that the exchanged information is usually delayed due to the time elapsed during the data transmission process. Using classical dynamic average consensus, delays may lead to poor performance or even instability. In this paper, we propose a novel dynamic consensus method that counteracts the negative effects of delays by means of delay compensation techniques. The interest of our dynamic consensus method with delay compensation is that it converges under mild conditions on graph connectivity and bounded reference signals, no matter how large the delays are, as long as delays are fixed and known. We also provide a formal characterization of the convergence speed of our method. Additionally, our results apply to fixed directed strongly connected, and undirected topologies.

Distributed fixed-time and prescribed-time average consensus for multi-agent systems with energy constraints

This paper is devoted to the investigation of the fixed-time and prescribed-time average consensus problem for multi-agent systems (MASs) with energy constraints. For the purpose of the fixed-time and prescribed-time average consensus of MASs, two control algorithms are firstly developed in this paper on the basis of the fixed-time technique and the prescribed-time technique. Then, the time integral of the quadratic function is constructed to calculate the energy consumption by the error between each agent and the reference state. And the sufficient conditions for the stability of the Lyapunov function are provided by designing the parameters and the gain matrix. Ultimately, the theoretical results are effectively and correctly verified by utilizing simulations.

Privacy Preserving via Secure Summation in Distributed Kalman Filtering

Average consensus is a major operation in distributed Kalman filtering. It requires neighboring nodes to exchange state information with each other, which may result in undesirable private data leakage. Since distributed Kalman filtering requires that the estimate at each time instant is accurate, it brings more challenges to design privacy-preserving scheme for operation. In this article, we design a privacy-preserving scheme for distributed Kalman filtering without the loss of estimation performance, which is also suitable for average consensus or dynamic average consensus of multiagent systems. We first build a secure multihop communication based on an encryption scheme. We then calculate the sum of the states of neighboring nodes with secure summation, which ensures that the state update will not reveal the state of the node to its neighboring nodes. We employ different methods to calculate the sum of the states of neighboring nodes against noncollusive and collusive adversaries. For the noncollusive case, the privacy of the honest nodes is preserved. For the collusive case, if there are too many adversaries, the privacy of the honest nodes could be exposed when accurate distributed Kalman filtering is accomplished. Therefore, we measure the risk of the global system suffering privacy leakage as the privacy index and improve the ability of the system to defend the collusive adversaries by using a group-based method. Some numerical examples are provided to illustrate the effectiveness of the proposed schemes.

Analysis of average consensus of multi-agent systems with time delay using packet selection and synchronization protocol

A stabilization approach for the average consensus of homogeneous multi-agent systems (MASs) entailing non-uniform, asymmetric, and time-varying delays in communication networks is proposed. Agents are modeled using a continuous-time linear dynamical system with a local discrete-time controller. To address communication delays, we propose two logic functions, namely, a packet selection algorithm that constantly selects and updates the latest received control signal packets and a synchronization algorithm that asymmetrically synchronizes these packets to preserve the average value of state variables. Based on the Lyapunov theory, we establish a linear matrix inequality (LMI) condition that sufficiently guarantees the stability and consensus of the overall system. To establish this condition, only the time delay upper bound is required, without any special assumptions on the delay pattern. We thoroughly discuss the proposed algorithm and evaluate its effectiveness via numerical simulations.

A hybrid protocol for the average consensus of multi-agent systems with impulse time window

In this paper, a control protocol is proposed, which can make the states of multi-agent systems achieve average consensus over time. Periodicity is one of the characteristics of the control protocol, we divide each period of the control protocol into three parts, some continuous inputs for part 1, an impulse input with time window for part 2, and another continuous inputs for part 3. The system is called A hybrid protocol for the average consensus of multi-agent systems with impulse time window. We study the control protocol by constructing the Lyapunov function and mathematical induction method. We give a strict mathematical proof of the feasibility of the control protocol. At the end of this paper, we use a numerical example to simulate the control protocol, and the experimental results are excellent. Due to impulse time window is considered in each control protocol, the control protocol in this paper is closer to reality and can be better applied to industrial production, which makes the control protocol more practical and efficient.

Event-Triggered Average Consensus of Multiagent Systems with Switching Topologies

Event-triggered average consensus of multiagent systems with switching topologies is studied in this paper. A distributed protocol based on event-triggered time sequences and switching time sequences is designed. Based on the inequality technique and stability theory of differential equations, a sufficient condition for achieving average consensus is obtained under the assumption that switching signal is ergodic and the total period over which connected topologies is sufficiently large. A numerical simulation is presented to show the effectiveness of the theoretical results.

基于事件的具有时变时滞多智能体系统的平均一致性

基于事件的具有时变时滞多智能体系统的平均一致性

Average Consensus by Graph Filtering: New Approach, Explicit Convergence Rate, and Optimal Design

This paper revisits the problem of multi-agent consensus from a graph signal processing perspective. Describing a consensus protocol as a graph spectrum filter, we present an effective new approach to the analysis and design of consensus protocols in the graph spectrum domain for the uncertain networks, which are difficult to handle by the existing time-domain methods. This novel approach has led to the following new results in this paper: 1) New necessary and sufficient conditions for both finite-time and asymptotic average consensus of multi-agent systems. 2) Direct link between the consensus convergence rate and the periodic consensus protocols. 3) Conversion of the fast consensus problem to the problem of polynomial design of graph spectrum filter. 4) A Lagrange polynomial interpolation method and a worst-case optimal interpolation method for the design of periodic consensus protocols for the MASs on uncertain graphs. 5) Explicit formulas for the convergence rate of the designed protocols. Several numerical examples are given to demonstrate the validity, effectiveness and advantages of these results.

Bounded average consensus for multi-agent systems with switching topologies by event-triggered persistent dwell time control

This paper is concerned with the consensus of multi-agent systems (MASs) with switching topologies. A norm-bounded event-trigger is designed where non-global information of the communication graph is involved. By directly employing the asynchronous event-triggered neighbor state information, a distributed persistent dwell time (PDT) based predictor-like consensus protocol is proposed. By the proposed scheme, the dynamics of local subsystems are allowed to be unstable during fast switching time intervals as well as the jump time instants, meanwhile, the bounded average consensus of overall MASs can be achieved. In addition, the Zeno-phenomena is naturally excluded. Numerical example is provided to demonstrate the effectiveness of the proposed method.

Optimal Dynamic Average Consensus of Multi-Agent Systems

This paper studies the average tracking problem on the premise of networked systems where each node is only allowed to interact with its neighbours. To this end, new dynamic average consensus protocols for both continuous-time and discrete time systems are proposed, and the corresponding analysis provides the rules on the optimal settings of the proposed algorithm with respect to convergence rate and steady-state performance. Numerical simulations validate the theoretical contributions of this work.

Fast convergent average consensus of multiagent systems based on community detection algorithm

In this paper, we consider fast convergent average consensus based on community detection algorithm. Generally, we know that a small network can have a faster convergence speed than a big one at the same condition. So we divide a multiagent network into several small networks. Firstly, let every small network reach own consensus, and then entire network reach the average consensus. Based on this idea, we present FCWAC Algorithm. For the FCWAC Algorithm, we obtain the results on the average consensus of first-, second-, and high-order continuous-time multiagent systems. Finally, simulation examples illustrate our theoretical results.

Average Consensus of Multi-Agent Systems Under Directed Topologies and Binary-Valued Communications

This paper is concerned with the average consensus problem of multi-agent systems with binary-valued communications under directed topologies. The information that each agent communicates with others is binary-valued with a fixed threshold and the control is a quantity of transportation from one agent to another, which causes that a decrease of the transmitter’s state leads to an increase of the same amount of the receiver’s. Due to the limitation of the information obtained, each agent needs to wait for a period of time to collect enough binary-valued information to estimate. Hence, we construct a two-scale control algorithm. At the small-time scale, each agent estimates its neighbors’ states based on the accumulation of binary-valued information for a period of time, during which the control is zero. At the large-time scale, each agent designs the control signal based on the estimation, which results in state updating of the multi-agent system. Then, each agent estimates the new states of its neighbors and the process of alternating estimation and control will be repeated. Finally, we prove that the estimation is convergent and give the mean square convergence rate as the reciprocal of the estimation time. More importantly, the multi-agent system is proved to achieve average consensus and the consensus speed is given. Simulation results are given to validate the algorithm.

Further Improvement of Fixed-Time Protocol for Average Consensus of Multi-Agent Systems

In this paper, a new class of fixed-time consensus protocols are proposed to further improve the existing fixed-time consensus protocols. Different from the existing fixed-time consensus protocols, both design parameters and the selected protocol function of the presented protocols determine the convergence rate and control input. Therefore, the presented protocol provides more flexibility to improve convergence rate and reduce control input. It is proved that the presented protocol can achieve fixed-time average consensus for multi-agent system with interaction topology modeled as undirected and directed graph. Numerical simulations are performed to demonstrate that the presented protocol can improve the convergence rate of the existing fixed-time consensus protocols without increasing control input.

A PD-Like Protocol With a Time Delay to Average Consensus Control for Multi-Agent Systems Under an Arbitrarily Fast Switching Topology.

This paper is concerned with the problem of average consensus control for multi-agent systems with linear and Lipschitz nonlinear dynamics under a switching topology. First, a proportional and derivative-like consensus algorithm for linear cases with a time delay is designed to address such a problem. By a system transformation, such a problem is converted to the stability problem of a switched delay system. The stability analysis is performed based on a proposed Lyapunov-Krasoversusii functional including a triple-integral term and sufficient conditions are obtained to guarantee the average consensus for multi-agent systems under arbitrary switching. Second, extensions to the Lipschitz nonlinear cases are further presented. Finally, numerical examples are given to illustrate the effectiveness of the results.

Average consensus of multi-agent systems with self-triggered controllers

In this paper the average consensus problem of multi-agent systems under the event-triggered controller is investigated. In event-triggered consensus, an event of each agent occurs only when the state measurement error exceeds a given threshold, and then the agent communicates with its neighbors and updates the control input at the event time. By appropriate event design, discontinuous threshold is employed and thus continuous communication among agents can be avoided to reduce energy consumption in practice. To avoid Zeno behavior, an event/time hybrid trigger is proposed to guarantee asymptotic consensus while excluding Zeno behaviors. Moreover, we develop a self-triggered algorithm to determine the event time instants to further avoid continuous self-state monitoring. Simulation results illustrate the effectiveness of the proposed approach.

Decentralized event-triggered average consensus for multi-agent systems in CPSs with communication constraints

The paper investigates decentralized event-triggered average consensus problem for multi-agent systems in cyberphysical systems (CPSs) with communication constraints. To reduce communication burden and improve the communication efficiency of multi-agent systems in CPSs, event-trigger is distributed at subsystem/agent level. A multi-agent system is then modeled as a reduced dimension hybrid system by taking into account decentralized event-triggered mechanism, communication delays and data dropouts within one framework. Some sufficient conditions for average consensus of each agent and an upper bound of communication delay and maximal allowable number of successive data dropouts (MANSD) are obtained, which can conveniently provide the relationship between the triggering parameters, communication constraints and the system stability. Specially, the quantitative relationship between the triggering parameters, MANSD and the system stability is derived. Finally, simulation results are given to illustrate the effectiveness of the proposed method.