Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics

Fu, Qing; Gu, Panpan; Li, Xiangdong; Wu, Jianrong

doi:10.1007/s11432-016-0341-7

Cited by 14 publications

(13 citation statements)

References 7 publications

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“…en, the mathematical model of a lifting subsystem is established based on existing literature studies about valve-controlled cylinders [6]. e mathematical model of the entire lifting system is established based on the complex system theory [7][8][9]. In this model, the coupling of lifting subsystems' information interacted through DTUs is also considered.…”

Section: Introductionmentioning

confidence: 99%

Design and Advanced Control of Intelligent Large-Scale Hydraulic Synchronization Lifting Systems

Dong

Qiu

Nguang

2019

Journal of Control Science and Engineering

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Lifting systems are in great demand since more and more massive buildings or bridges tend to be shifted or lifted integrally. Hydraulic cylinders in traditional lifting systems are usually supplied by a common pump with an oil tank, which brings long distance hydraulic pipes and signal lines. This paper designs a new architecture of an intelligent lifting system, with self-contained hydraulic power supply system, wireless communication modules, and distributed controller. Based on the designed architecture of the intelligent lifting system, an advanced iterative learning control strategy is proposed to enhance its synchronization performance. With the proposed advanced control strategy, synchronization is achieved in a finite time interval even under the effect of communication time delays and saturations. The distributed controller of a lifting subsystem only uses the delayed information received from subsystems around. This is distinguished from traditional lifting systems, in which all of the lifting subsystems are normally controlled in a centralized way.

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Section: Introductionmentioning

confidence: 99%

Design and Advanced Control of Intelligent Large-Scale Hydraulic Synchronization Lifting Systems

Dong

Qiu

Nguang

2019

Journal of Control Science and Engineering

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“…The asymptotic consensus cannot be satisfied for FOMASs with repetitive tasks, such as multi-mechanical arms on industrial production lines, and the requirement is to achieve complete consensus convergence within a limited time [24]- [27]. By now, many researchers have considered the consensus problem in a limited time by iterative learning control (ILC) and obtained fruitful results for integer-order MASs (IOMASs) [28]- [31]. In [28], ILC was applied to solve the problem of multi-agent system formation.…”

Section: Introductionmentioning

confidence: 99%

“…By now, many researchers have considered the consensus problem in a limited time by iterative learning control (ILC) and obtained fruitful results for integer-order MASs (IOMASs) [28]- [31]. In [28], ILC was applied to solve the problem of multi-agent system formation. P-type and D-type ILC are proposed for the consensus of multi-agent systems with regular linear dynamics and realized satisfactory tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Consensus Control of Fractional-Order Multi-Agent Systems With Time Delays via Fractional-Order Iterative Learning Control

Pan

et al. 2019

IEEE Access

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This paper considers the leader-following consensus tracking problem of fractional-order multi-agent systems (FOMASs) that contain communication time delays. By combining the iterative learning control (ILC) approach and fractional-order calculus, a distributed PD α-type fractional-order iterative learning control (FOILC) and a distributed D α-type FOILC algorithm are proposed, respectively. Based on graph theory and λ-norm theory, the consensus problems of the proposed algorithms are discussed, and their convergence conditions are identified. The theoretical analysis demonstrates that as the number of iterations increases, all fractional-order agents with communication time delays can realize consensus exactly on the desired output trajectory by selecting suitable gain matrices of FOILC over a finite time interval. Illustrative examples are presented, on which the performances of the proposed method are evaluated. INDEX TERMS Fractional-order system, multi-agent system, iterative learning control, consensus tracking, communication time delays.

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“…It is well known, as a powerful and simple control strategy, iterative learning control (ILC) received extensive attention since it was proposed in 1984 [3]. Classic ILC often requires the system can operate repeatedly so that the input signals can be continuously optimized along the iteration axis, and then achieve full tracking [4]- [10]. With the decades of development of ILC, it has been widely applied in distributed parameter systems (DPSs) [11]- [21].…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Discrete Distributed Parameter Systems With Randomly Varying Trial Lengths

2019

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In this paper, iterative learning control (ILC) is employed in discrete spatial-temporal parabolic distributed parameter systems (DPSs), where the trial lengths vary randomly. A distributed ILC strategy is proposed, in which containing spatial variable, utilizes all past tracking information to improve current performance. Through rigorous theoretical analysis, the convergence of the system output error is proved under mathematical expectation along the iteration axis. Finally, the proposed method is applied to numerical simulation to illustrate its effectiveness. INDEX TERMS Iterative learning control, distributed parameter systems, partial difference equations, random, convergence.

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Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics

Cited by 14 publications

References 7 publications

Design and Advanced Control of Intelligent Large-Scale Hydraulic Synchronization Lifting Systems

Design and Advanced Control of Intelligent Large-Scale Hydraulic Synchronization Lifting Systems

Consensus Control of Fractional-Order Multi-Agent Systems With Time Delays via Fractional-Order Iterative Learning Control

Iterative Learning Control for Discrete Distributed Parameter Systems With Randomly Varying Trial Lengths

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