Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm

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

doi:10.1016/j.jfranklin.2018.04.033

Cited by 37 publications

(31 citation statements)

References 33 publications

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“…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. In [40], the consensus problem of continuous distributed parameter MASs with time delay is studied.…”

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%

See 2 more Smart Citations

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%

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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 [31], a PI-type ILC was applied to both linear and nonlinear fractional-order MAS to solve consensus tacking problem. In very recently, the study on the consensus control via ILC for MAS with DPS has also received considerable attention [32], [33]. Based on the framework of network topologies, a consensus-based ILC protocol was proposed for a class of MAS described with DPS in [32].…”

Section: Introductionmentioning

confidence: 99%

“…In very recently, the study on the consensus control via ILC for MAS with DPS has also received considerable attention [32], [33]. Based on the framework of network topologies, a consensus-based ILC protocol was proposed for a class of MAS described with DPS in [32]. The consensus control problem of distributed parameter models MAS with time-delay was considered in [33], and the conclusions were also extended to Lipschitz nonlinear case.…”

Section: Introductionmentioning

confidence: 99%

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Liu

Lan

2020

IEEE Access

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This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PItype iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples. INDEX TERMS Multi-agent systems, iterative learning control, Gronwall inequality, hyperbolic distributed parameter system.

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Iterative learning control for hyperbolic distributed parameter systems based on sensor–actuator networks

Zhang

Cui

Lou

et al. 2022

Asian Journal of Control

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This paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor–actuator networks which can be described by hyperbolic partial differential equations. A D‐type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD‐type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor–actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods.

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Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm

Cited by 37 publications

References 33 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

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Iterative learning control for hyperbolic distributed parameter systems based on sensor–actuator networks

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