Consensus Control For Multi-agent Systems Research Articles

Adaptive expected-time tracking consensus control for multiagent systems with external disturbances and time delays

This paper studies the expected-time tracking control problem for multiagent systems with disturbances and time delays. The expected-time control scheme can ensure that control objectives can be completed within the desired time. In the other words, the output signal can track the reference signal in an expected time. Based on a special error and the adaptive technology, the expected-time tracking consensus control scheme is achieved. Moreover, by using the neural networks technique, the unknown nonlinear functions which often exist in the dynamic of the system are handled. According to the Lyapunov stability theorem and the Lyapunov–Krasovskii function, it is proved that the consensus errors are semi-globally uniformly ultimately bounded. At last, the feasibility of the control method is verified by a simulation example.

Guaranteed Performance Event-Triggered Adaptive Consensus Control for Multiagent Systems under Time-Varying Actuator Faults

Guaranteed Performance Event-Triggered Adaptive Consensus Control for Multiagent Systems under Time-Varying Actuator Faults

Prescribed Time Consensus Control of Multiagent Systems With Minimized Time-Varying Cost Function

Prescribed Time Consensus Control of Multiagent Systems With Minimized Time-Varying Cost Function

Optimal Consensus Control for Multi-Agent Systems With Unknown Dynamics and States of Leader: A Distributed KREM Learning Method

Optimal Consensus Control for Multi-Agent Systems With Unknown Dynamics and States of Leader: A Distributed KREM Learning Method

On Generalized Homogeneous Leader-Following Consensus Control for Multiagent Systems

On Generalized Homogeneous Leader-Following Consensus Control for Multiagent Systems

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Event-Triggered Guaranteed Cost Secure Consensus Control of Multiagent Systems Under DoS Attacks Over Multiple Communication Channels

Refined Dynamic Event-Triggering Cluster Consensus of Multiagent Systems With Fixed/Switching Topology.

This article is concerned with cluster consensus control of multiagent systems (MASs) with the fixed/switching topology under a dynamic event-trigger (DET) mechanism. A refined sampled-data-based DET scheme is proposed by introducing two dynamically adjusting threshold parameters to distinguish the different transmission requirements for neighboring agents intra and outer cluster. Faced with the difficulties of acquiring full state information among spatially distributed agents, output feedback is employed to construct cooperative control protocols. Both fixed and switching topologies are considered to execute the designed DET-based cooperative cluster consensus control protocols. By constructing appropriate Lyapunov-Krasovskii functionals (LKFs), some sufficient criteria in terms of matrix inequalities for the cluster consensus of MASs are derived, which can ensure that the error system with the proposed DET-based control strategy is asymptotically stable. Facing the nonconvex issue induced by output feedback, a particle swarm optimization (PSO)-based control design algorithm is novelly developed to calculate the control gains and event-triggering parameters jointly based on the derived stability criteria. The elements of the matrix variables are valued stochastically in certain ranges and the fitness function is designed as the accumulation of the weighting value of each matrix inequality. Finally, an application of multiple satellites formation flying is applied to numerically illustrate the effectiveness of the cluster consensus control strategy with the designed DET mechanism.

Consensus Control of Multi-Agent Systems With Different State Constraints and Event-Triggered Communication

Consensus Control of Multi-Agent Systems With Different State Constraints and Event-Triggered Communication

Consensus control for multi-agent systems with input constraints based on disturbance observer

In this paper, a second-order-disturbance-observer-based distributed consensus control scheme is designed for a class of nonlinear multi-agent systems with input constraints. The second-order disturbance observer tracks the disturbance better than the common first-order disturbance observer. This paper provides the analysis of two kinds of disturbance observers and then compares the results from a simulation of the two types of observers. In the controller design process, a hyperbolic tangent function is used to limit output feedback in the control law. The overall closed-loop system reaches global stability with disturbance and the tracking errors exponentially converge without disturbance. The effectiveness of the proposed control law is illustrated by simulation results.

Leader-following Adaptive Guaranteed-performance Consensus Control for Multi-agent Systems With Exogenous Disturbance

Leader-following Adaptive Guaranteed-performance Consensus Control for Multi-agent Systems With Exogenous Disturbance

Private bipartite consensus control for multi-agent systems: A hierarchical differential privacy scheme

This paper addresses the differentially private bipartite consensus control problem for general linear discrete-time multi-agent systems (MASs), where the initial states of the agents are the sensitive data to be protected from potential eavesdroppers. In response to the cooperative–competitive relationship among the agents, a novel hierarchical differential privacy mechanism (HDPM) is proposed, which consists of a classifier and a pair of input and output noise injectors. The HDPM and the bipartite consensus controller are co-designed to ensure the ultimate mean-square bipartite consensus while preserving differential privacy. Finally, numerical simulations are conducted to illustrate the effectiveness of the proposed algorithm.

Guaranteed-performance consensus control for multi-agent systems with external disturbances via event-triggered strategy

In this paper, we investigate the leader-following guaranteed performance consensus problem for multi-agent systems(MASs) controlled by event-triggered under external disturbances. First, a disturbance observer is designed for each follower, which can be used to efficiently estimate external disturbances. Second, to reduce the waste of communication resources, a new distributed event-triggered strategy based on the state estimation of neighboring agents is presented, which does not need continuous communication among agents. And the growing estimation error is reset by communication updates. The analysis illustrates that Zeno behavior can be effectively averted by formulating a lower bound on the number of normals for the minimum inter-event interval. Also, based on the above proposed state feedback consensus protocol with guaranteed performance, sufficient conditions are derived to guarantee that a MASs can achieve leader-following consensus. In addition, the consensus criterion with guaranteed performance is put forward in the form of a linear matrix inequality. The cost of guaranteed performance with leader-following structural properties is also established. At last, an example of numerical simulation is presented to confirm the derived analytical solution.

Failure-distribution-dependent leader-following fault-tolerant consensus for multiagent system with cyberattacks

This article concentrates on a failure-distribution-dependent leader-following fault-tolerant consensus control for multiagent systems with cyberattacks. First, considering the characteristic of failures, a failure distribution model which is more in line with the actual situation is established. To reduce the influence of zero initial conditions, an [Formula: see text] performance index suitable for leader-following multiagent systems is constructed. After this, it is difficult to obtain the augmented form of the closed-loop system due to the coupling of parameters caused by cyberattacks. Thus, based on the provided failure model and performance index, a new fault-tolerant controller is designed by using topological structure segmentation to satisfy the leader-following multiagent system fault-tolerant consensus. Finally, a numerical example is shown to prove the feasibility of this algorithm.

Adaptive Event-Triggered Consensus of Multi-Agent Systems in Sense of Asymptotic Convergence.

In this paper, the asymptotic consensus control of multi-agent systems with general linear agent dynamics is investigated. A neighbor-based adaptive event-triggering strategy with a dynamic triggering threshold is proposed, which leads to a fully distributed control of the multi-agent system, depending only on the states of the neighboring agents at triggering moments. By using the Lyapunov method, we prove that the states of the agents converge asymptotically. In addition, the proposed event-triggering strategy is proven to exclude Zeno behavior. The numerical simulation results illustrate that the agent states achieve consensus in sense of asymptotic convergence. Furthermore, the proposed strategy is shown to be scalable in case of variable agent numbers.

Prescribed Time Fuzzy Adaptive Consensus Control for Multiagent Systems With Dead-Zone Input and Sensor Faults

Prescribed Time Fuzzy Adaptive Consensus Control for Multiagent Systems With Dead-Zone Input and Sensor Faults

Data-Based Optimal Consensus Control for Multiagent Systems With Time Delays: Using Prioritized Experience Replay

Data-Based Optimal Consensus Control for Multiagent Systems With Time Delays: Using Prioritized Experience Replay

Adaptive Optimal Consensus Control of Multiagent Systems With Unknown Dynamics and Disturbances via Reinforcement Learning

Adaptive Optimal Consensus Control of Multiagent Systems With Unknown Dynamics and Disturbances via Reinforcement Learning

PDE-Based Boundary Adaptive Consensus Control of Multiagent Systems With Input Constraints.

The leader-follower adaptive consensus control problem is addressed for partial differential equations (PDEs) multiagent systems (MASs), and these agents are composed of flexible manipulator systems with input nonlinearity, boundary uncertainties, and time-varying disturbances. Because of the spatial variables in the model, the design of adaptive protocols is more difficult than that of ordinary differential equation (ODE) MASs. By designing the Lyapunov function, a novel distributed boundary control (BC) protocol is constructed, which not only ensures the consensus of angular positions but also suppresses the boundary vibration of each agent. The hybrid effects of dead zones and input saturation on flexible manipulator systems are addressed using the approximation properties of neural networks (NNs). In addition, the disturbance adaptive laws are proposed to provide a control solution for bounded and time-varying disturbances. Furthermore, by applying the Lyapunov stability theory, the uniformly bounded stability of the multiflexible manipulator can be ensured. Finally, the feasibility of the presented control approach is verified using numerical examples.

Human-in-the-Loop Consensus Control for Multiagent Systems With External Disturbances.

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

Output Consensus Control of Multi-Agent Systems With Switching Networks and Incomplete Leader Measurement

Output Consensus Control of Multi-Agent Systems With Switching Networks and Incomplete Leader Measurement