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

Dai, Xisheng; Wang, Cun; Tian, Senping; Huang, Qingnan

doi:10.1016/j.jfranklin.2019.05.015

Cited by 60 publications

(51 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark 1: Parabolic DPS and hyperbolic DPS are two kinds of the most important PDEs. Studies ILC-based consensus for the MAS with parabolic DPS has been widely investigated [33], [34], while ILC-based consensus for the MAS with hyperbolic DPS are limited. In this paper, ILC technique is applied to the consensus control of MAS described by a class of second-order hyperbolic DPS.…”

Section: The Communication Graph Can Be Represented By Two Types Of Mmentioning

confidence: 99%

“…Remark 2: PI-type ILC protocol (7) is borrowed from [31]. When = 0, it is degenerated to the case of [25], [33], but without initial state learning. That is to say, the proposed control algorithm is an extension of the learning control algorithm developed in [25], [33].…”

Section: The Communication Graph Can Be Represented By Two Types Of Mmentioning

confidence: 99%

“…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%

“…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. In [34], [35], a second-order iterative learning consensus control protocol was proposed for MAS with parabolic DPS or hyperbolic DPS.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Liu

Lan

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: The Communication Graph Can Be Represented By Two Types Of Mmentioning

confidence: 99%

Section: The Communication Graph Can Be Represented By Two Types Of Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu

Lan

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Iterative learning control (ILC) has been widely utilized to cope with the repeated tracking control with high precision requirement in the fixed time interval due to its simplicity and effectiveness [8], [9]. Hence, ILC has been successfully implemented to many kinds of multi-agent systems in recent references, such as high-order nonlinear MASs [10], singular MASs [11], fractional-order MASs [12], and distributed parameter MASs [13]- [15], etc.. In [16], [17], the formation control problems of nonlinear MASs under switching interaction topologies were addressed by employing the ILC scheme.…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Wang

Zhou

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper investigates the leader-following consensus tracking problems via iterative learning control for singular fraction-order multi-agent systems in the presence of iteration-varying switching topologies and initial state errors. First, in order to eliminate the impulsive effect of singular systems and handle iteration-varying topologies, the closed-loop D α-type iterative learning control protocol is proposed. To deal with initial state errors, the initial state learning laws are introduced in light of the initial output errors of each follower agent. The developed D α-type learning protocols based on initial state learning laws can guarantee each follower track perfectly the leader agent in the fixed time interval. Next, the sufficient convergent conditions of consensus tracking errors are provided. Moreover, the D α-type learning protocols are extended to nonlinear singular fraction-order multi-agent systems with iteration-varying topologies and initial state errors. Finally, two numerical examples are presented to verify the validity of the proposed D α-type learning scheme in this paper.

show abstract

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Lan

Zheng

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

This article presents a PI-type iterative learning control (ILC) law with initial state learning for a class of 𝛼 (0 < 𝛼 ≤ 1) fractional order two-dimensional (2D) linear systems. First, by using backward difference method, the finite difference approximation of the fractional order derivative is obtained, which leads to globally 2 − 𝛼 order accuracy. Then, a PI-ILC law is constructed at the nodes and the convergence analysis of the iterative scheme is proved. A linear matrix inequality-based sufficient condition is derived to guarantee that the tracking error is asymptotically convergent. The obtained convergence condition is fractional order dependent. Most of the classical ILC conditions for fractional order one-dimensional linear systems fall into the special cases of this article. Finally, the simulation results show the effectiveness of the proposed control method.

show abstract

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

Cited by 60 publications

References 27 publications

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

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

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Contact Info

Product

Resources

About