Event-triggered Iterative Learning Control for Perfect Consensus Tracking of Non-identical Fractional Order Multi-agent Systems

Wang, Liming; Zhang, Guoshan

doi:10.1007/s12555-019-0882-y

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…where δk (⋅, t) = y k j (⋅, t) − y k i (⋅, t) is the consensus error of any two agents in the MASs (1).…”

Section: Ilc Protocol Design and Convergence Analysismentioning

confidence: 99%

“…In recent years, according to the communication structure characteristics of MASs, many control protocols have been proposed to achieve its consensus. 1 Therefore, the consensus control problem has been gradually discussed and extensively studied. [2][3][4] At the same time, the related research results of consensus control have also been widely used in practice.…”

Section: Introductionmentioning

confidence: 99%

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Closed‐loop consensus control of partial difference multi‐agent systems via variable gain iterative learning strategy

Wang

Zhou

Liu

2022

Intl J Robust & Nonlinear

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In the research on consensus control of multi-agent systems (MASs), it is the key to ensure that the state quantities (speed, displacement, etc.) of all agents tend to be consensus. Under this premise, how to accelerate the convergence rate of the consensus error is also an important issue. Aiming at the accelerated consensus problem of MASs composed of partial difference equations, based on the network topology and the output form of each follower, a distributed closed-loop accelerated iterative learning control (ILC) protocol with variable gain was proposed, which was designed to improve the speed of consensus error. With the help of basic mathematical tools such as discrete Gronwall inequality and contraction mapping method, the sufficient conditions for the convergence of the consensus error are derived and analyzed. Finally, numerical simulations show that the proposed acceleration control protocol is more effective than the traditional open-loop and closed-loop ILC protocols.

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“…where δk (⋅, t) = y k j (⋅, t) − y k i (⋅, t) is the consensus error of any two agents in the MASs (1).…”

Section: Ilc Protocol Design and Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Closed‐loop consensus control of partial difference multi‐agent systems via variable gain iterative learning strategy

Wang

Zhou

Liu

2022

Intl J Robust & Nonlinear

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“…Several other research efforts have explored ET-ILC for linear systems [33,34]. In addition, Zhang et al [35], Lin et al [36,37], Hua et al [38] and Wang et al [39] developed ET-ILC methods for multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive event‐triggered ILC scheme design for discrete‐time non‐repetitive nonlinear systems

Chai,

Yu,

Zhao

2023

IET Control Theory & Appl

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In this study, a robust adaptive event‐triggered iterative learning control (ET‐ILC) method is developed for discrete‐time non‐repetitive nonlinear systems with both time‐varying system parameters and time‐iteration‐varying non‐parametric uncertainty. The proposed method utilizes an iteration‐varying dead‐zone function to estimate both parametric and non‐parametric uncertainties simultaneously. Moreover, an event‐triggered control mechanism including a mixed triggering strategy is employed to minimize the unnecessary usage of communication resource. This strategy updates the controller only when the triggering condition is met, thereby reducing the frequency of controller actions. The results are also extended to a class of multiple‐input–multiple‐output (MIMO) non‐repetitive nonlinear systems. The designed robust adaptive ET‐ILC algorithm not only guarantees perfect learning convergence in the iteration domain but also contributes to saving the system resources. The effectiveness of the presented method is demonstrated by virtue of the rigorous theoretical analysis and the illustrative simulation examples.

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“…The eventtriggered control method has been proposed by Tabuada in [14], and was first used for fractional-order multiagent systems in [15]. Many outstanding results have been published recently [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Quasiconsensus of fractional-order heterogeneous multiagent systems under event-triggered impulsive control method

2022

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This paper investigates the quasiconsensus problem of fractional-order heterogeneous multiagent systems, the distributed impulsive control protocol is designed for the multiagent system. In contrast to some existing results, the impulsive moments are determined by preset events, i.e., the event-triggered mechanism is used. Based on the fractional-order Lyapunov stability theory and fractional-order differential inequality, the quasiconsensus criteria are derived; furthermore, the prescribed error bound is given. Then, Zeno behavior for the considered event-triggered control method is excluded. Finally, numerical examples are given to shown the effectiveness of the proposed method.

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Event-triggered Iterative Learning Control for Perfect Consensus Tracking of Non-identical Fractional Order Multi-agent Systems

Cited by 9 publications

References 37 publications

Closed‐loop consensus control of partial difference multi‐agent systems via variable gain iterative learning strategy

Closed‐loop consensus control of partial difference multi‐agent systems via variable gain iterative learning strategy

Robust adaptive event‐triggered ILC scheme design for discrete‐time non‐repetitive nonlinear systems

Quasiconsensus of fractional-order heterogeneous multiagent systems under event-triggered impulsive control method

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