Stochastic fixed-time consensus problem of multi-agent systems with fixed and switching topologies

Zhao, Lingzhi; Sun, Yongzheng; Dai, Haifeng; Zhao, Donghua

doi:10.1080/00207179.2020.1736640

Cited by 15 publications

(17 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we design controller (2) with noises. More detailed explanation can be found in [29][30][31][32][33][34].…”

Section: Consensus Protocolmentioning

confidence: 99%

“…Definition 1. (Consensus with probability one [29][30][31][32][33][34] : For system (3), the system is said to be consensus with probability one if for any initial states, there exists ax such that lim…”

Section: Consensus Protocolmentioning

confidence: 99%

“…(33) means that the upper bound of E[T (x, w)] is independent of initial state x 0 , and it is an increasing function with respect to parameter in the open interval (0,1) according to Remark 5.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Yan

2020

IET Control Theory & Appl

View full text Add to dashboard Cite

The paper devotes to study fixed‐time stability of stochastic nonlinear systems and fixed‐time consensus of stochastic multi‐agent systems (SMASs). Firstly, a new fixed‐time stability theorem is established and a high‐precise estimation of settling time is provided. Secondly, as an application, the fixed‐time consensus problem of SMASs is discussed in virtue of the established theorem. A new class of fixed‐time nonlinear consensus protocols with stochastic perturbation is designed by employing the neighbour's information. Based on graph theory, matrix theory and fixed‐time stability theorem, we prove that fixed‐time consensus of SMASs is achieved under the designed protocol. Moreover, some sufficient conditions are proposed to guarantee fixed‐time consensus of SMAS. It is shown that the settling time is not only independent of the initial conditions, but also it has higher precise. In the end, three numerical examples are provided to illustrate correctness of our theoretical results.

show abstract

“…Therefore, we design controller (2) with noises. More detailed explanation can be found in [29][30][31][32][33][34].…”

Section: Consensus Protocolmentioning

confidence: 99%

Section: Consensus Protocolmentioning

confidence: 99%

See 1 more Smart Citation

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Yan

2020

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…Even when the initial conditions tend to infinity, the convergence time will grow indefinitely. In order to solve this problem, fixed-time cooperative approaches (Huang and Jia, 2018; Mishra et al, 2020; Tian et al, 2019; Zhao et al, 2020a) have been put forward, where the convergence is achieved within a specified time which is predetermined but has nothing to do with any initial conditions. A distributed fixed-time protocol was proposed to implement average-tracking for MASs under state-dependent communication topology in Hong et al (2017), where multiple reference signals have bounded derivatives.…”

Section: Introductionmentioning

confidence: 99%

Distributed fixed-time average-tracking for multi-agent systems with mismatched and matched disturbances

Liu

Zhang

et al. 2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, distributed fixed-time average-tracking problem is discussed for second-order multi-agent systems (MASs) with mismatched and matched disturbances. To address this issue, we first propose a novel distributed fixed-time average estimator for each agent to estimate the average value of multiple time-varying reference signals (TVRSs). Then, to tackle with the case in the presence of external mismatched and matched disturbances, a disturbance observer is constructed to estimate them in a fixed time. Finally, based on the disturbance observer, a fixed-time average-tracking protocol is designed to drive each agent to track the average value of multiple TVRSs within a fixed time. Simulation experiments are conducted to verify the effectiveness of the proposed fixed-time anti-disturbance average-tracking algorithm.

show abstract

“…Therefore, the constrained switching is more applicable and preferable in practical applications. The latest literatures in analysis of constrained switching have been mainly concentrated on state-dependent, time-dependent, and a combination of them [22]. As one of the most components of constrained switching, the time-dependent switching has been extensively studied because of its wide applications.…”

Section: Introductionmentioning

confidence: 99%

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Cheng

Song

Xu³

et al. 2022

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

show abstract

Stochastic fixed-time consensus problem of multi-agent systems with fixed and switching topologies

Cited by 15 publications

References 41 publications

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Distributed fixed-time average-tracking for multi-agent systems with mismatched and matched disturbances

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Contact Info

Product

Resources

About

Stochastic fixed-time consensus problem of multi-agent systems with fixed and switching topologies

Cited by 15 publications

References 41 publications

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Fixed‐time stability of stochastic nonlinear systems and its application into stochastic multi‐agent systems

Distributed fixed-time average-tracking for multi-agent systems with mismatched and matched disturbances

H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Contact Info

Product

Resources

About

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning