Consensus control for multi-agent systems with distributed parameter models

Fu, Qing; Du, Lizhi; Xu, Guangzhao; Wu, Jianrong; Yu, Pengfei

doi:10.1016/j.neucom.2018.04.051

Cited by 32 publications

(9 citation statements)

References 23 publications

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“…Recently, there have been reports on related research on distributed parameter MASs. For example, in [38], the consensus of the continuous distributed parameter model MASs was achieved based on the Lyapunov function method. In [39], an ILC algorithm was proposed for continuous distributed parameter model MASs.…”

Section: Introductionmentioning

confidence: 99%

“…(1) It can be noted that the works in [37][38][39][40][41] present a type of consensus problem in the study of continuous distributed parameter MASs. However, due to the limited channel bandwidth in the communication system, the controller can only apply the sampling data obtained at discrete moments.…”

Section: Introductionmentioning

confidence: 99%

“…(2) In the works in [38][39][40][41], the consensus control problem of parabolic distributed parameter MASs is studied. Since hyperbolic system is an important system type, a kind of consensus control of hyperbolic distributed parameter MASs has been studied.…”

Section: Introductionmentioning

confidence: 99%

“…(3) From the analysis of the works in [38][39][40][41][42], in reality, the MASs have relatively complex nonlinearities and time delay. erefore, an ILC algorithm is designed, which contains consensus errors between any two agents.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Wang

Dai

et al. 2021

Complexity

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This paper considers the consensus control problem of nonlinear spatial-temporal hyperbolic partial difference multiagent systems and parabolic partial difference multiagent systems with time delay. Based on the system’s own fixed topology and the method of generating the desired trajectory by introducing virtual leader, using the consensus tracking error between the agent and the virtual leader agent and neighbor agents in the last iteration, an iterative learning algorithm is proposed. The sufficient condition for the system consensus error to converge along the iterative axis is given. When the iterative learning number k approaches infinity, the consensus error in the sense of the L 2 norm between all agents in the system will converge to zero. Furthermore, simulation results illustrate the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Wang

Dai

et al. 2021

Complexity

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“…In [10], [11], exact synchronization for a set of coupled wave processes, part of which equipped by a boundary control input, was provided in the two cases of Dirichlet and Neumann actuation. In [6], the leaderless consensus problem was addressed with reference to multi agent systems where agents dynamics are governed by heat and wave dynamics with distributed control.…”

Section: Introductionmentioning

confidence: 99%

Leader-follower synchronization and ISS analysis for a network of boundary-controlled wave PDEs

Aguilar¹,

Orlov²,

Pisano³

2020

Preprint

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A network of agents, modeled by a class of wave PDEs, is under investigation. One agent in the network plays the role of a leader, and all the remaining "follower" agents are required to asymptotically track the state of the leader. Only boundary sensing of the agent's state is assumed, and each agent is controlled through the boundary by Neumann-type actuation. A linear interaction protocol is proposed and analyzed by means of a Lyapunov-based approach. A simple set of tuning rules, guaranteeing the exponential achievement of synchronization, is obtained. In addition, an exponential ISS relation, characterizing the effects on the tracking accuracy of boundary and in-domain disturbances, is derived for the closed loop system.

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Consensus of multi‐agent system with reaction‐diffusion terms and time delays via output‐feedback adaptive boundary control

Wan,

Yan,

et al. 2024

Adaptive Control & Signal

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SummaryMulti‐agent systems (MASs) are widely used in many necessary fields such as cybersecurity, precision agriculture, satellites, and smart grids. In many practical processes, a proportion of states cannot be obtained directly. Therefore, it is desired to design an observer. This article explores the utilization of output‐feedback adaptive control techniques to achieve consensus in MASs based on partial differential equations (PDEs) with reaction‐diffusion terms and time delays. First, in dealing with time‐invariant delays, an observer according to the Luenberger method is proposed to assess the state of agents. Convergence conditions of the estimated states are obtained by employing Lyapunov functions and Wirtinger inequality. Using the obtained estimated states, a boundary adaptive control strategy is proposed to make the error system consensus. The merit of this control method lies in the fact that communication between agents only occurs at the spatial boundary position, not throughout the entire spatial domain, and the parameters of the system can be constantly changed. Consensus criteria of MASs with time‐invariant delays are obtained by using the output‐feedback adaptive control, and the results are further generalized to the model with time‐varying delays. Lastly, two experimental scenarios are provided to show the practicality of the proposed theories.

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Consensus control for multi-agent systems with distributed parameter models

Cited by 32 publications

References 23 publications

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Leader-follower synchronization and ISS analysis for a network of boundary-controlled wave PDEs

Consensus of multi‐agent system with reaction‐diffusion terms and time delays via output‐feedback adaptive boundary control

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