Iterative learning based consensus control for distributed parameter multi-agent systems with time-delay

Lan, Yong‐Hong; Wu, Bin; Shi, Yong; Luo, Yiping

doi:10.1016/j.neucom.2019.04.064

Cited by 33 publications

(26 citation statements)

References 22 publications

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“…where min (E + QΓP) is the minimum eigenvalue of the positive definite matrix E + QΓP. By (10) and 11, we have…”

Section: Resultsmentioning

confidence: 96%

Iterative learning control for one‐sided Lipschitz nonlinear singular conformable differential equations

Wang

2020

Intl J Robust & Nonlinear

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This article contributes to a detailed convergence analysis of iterative learning control for singular conformable differential equations with one-sided Lipschitz nonlinearity. In order to track the desired reference trajectory in a finite time interval, a closed-loop D-type learning update law is proposed for such nonlinear systems. Here, the strict convergence analysis is derived under the condition of identical initial state. A numerical example is provided to verify the effectiveness of the proposed controller. K E Y W O R D S convergence, iterative learning control, singular conformable differential equations 1 INTRODUCTION Iterative learning control is an accurate technology, which is suitable for the systems with repetitive motion in a finite time interval. Its goal is to achieve perfect tracking task. The concept of iterative learning control was first mentioned by Uchiyama, a Japanese scholar, in a paper on robot control. 1 A few years later, Arimoto and others formally proposed iterative learning control based on Reference 2. The principle of iterative learning control is: through the control attempt of the controlled system, the deviation between the output signal and the desired target is corrected to improve the tracking performance of the system. At the same time, it attracts a large number of researchers and has achieved a large number of scientific research results. 3-10 Singular system is a kind of system described by differential and algebraic equations. Compared with the regular system described only by differential or difference equations, it has algebraic equation description part. Because of different research fields, singular systems are called by different scholars in different fields, such as generalized state space system, descriptor system, and so on. The singular system includes the description of algebraic equation. Therefore, the applicability of singular system is much wider than that of normal system. The original singular system model was proposed by Ardema in 1962 when studied the dynamic model of spacecraft. Since then, the majority of research enthusiasts have carried out extensive research on singular systems, and obtained many very valuable theoretical results. In the field of Physics, Solimene et al 11 dealt with the calculation of the singular system of radiation operators related to the banded current case. And it can be easily used to describe biological systems in practice. 12,13 Ma and Boukas, 14 based on a singular

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“…where min (E + QΓP) is the minimum eigenvalue of the positive definite matrix E + QΓP. By (10) and 11, we have…”

Section: Resultsmentioning

confidence: 96%

Iterative learning control for one‐sided Lipschitz nonlinear singular conformable differential equations

Wang

2020

Intl J Robust & Nonlinear

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“…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%

“…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%

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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“…Lemma 7. For the heterogeneous multiagent systems (8) and (9), if and only if the Laplacian matrix L s has at least one zero eigenvalue, the real parts of the rest of eigenvalues are positive, and satisfy the condition of inequality (10), and the systems can achieve bipartite consensus.…”

Section: Bipartite Consensus Under Undirectedmentioning

confidence: 99%

“…Multiagent systems and its cooperative control [1] are widely used in many practical systems such as unmanned aerial vehicles (UAV) [2], monitoring and security [3], sensor network [4], and delay system [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Bipartite Consensus of Heterogeneous Multiagent Systems with Diverse Input Delays

Chen

Luo

et al. 2020

Complexity

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This paper investigates the bipartite consensus problem of heterogeneous multiagent systems with diverse input delays. Based on the systems composed of first-order and second-order agents, the novel control protocols are designed. Using frequency-domain analysis and matrix theory, the corresponding upper bounds of the allowable delays are obtained under the undirected topology and directed topology, respectively. Finally, simulation examples are given to verify the theoretical analysis.

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Iterative learning based consensus control for distributed parameter multi-agent systems with time-delay

Cited by 33 publications

References 22 publications

Iterative learning control for one‐sided Lipschitz nonlinear singular conformable differential equations

Iterative learning control for one‐sided Lipschitz nonlinear singular conformable differential equations

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

Bipartite Consensus of Heterogeneous Multiagent Systems with Diverse Input Delays

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