Adaptive iterative learning control for consensus of multi‐agent systems

Li, Jinsha; Li, Junmin

doi:10.1049/iet-cta.2012.0048

Cited by 81 publications

(62 citation statements)

References 25 publications

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“…It follows straightforwardly from Equation (3) that (Li & Li, 2013): Let the undirected graphḠ be connected. Then, it follows from Equation (4) that…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

2015

International Journal of Systems Science

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In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

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“…It follows straightforwardly from Equation (3) that (Li & Li, 2013): Let the undirected graphḠ be connected. Then, it follows from Equation (4) that…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

2015

International Journal of Systems Science

Self Cite

View full text Add to dashboard Cite

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“…Remark 1: In the literatures [31][32][33], the multi-agent systems should operate repetitively over a fixed time interval. Being different with [31][32][33], in this paper, the dynamic of each follower has parametric uncertainty. Then, to reject the parametric uncertainty, we should combine the distributed consensus problem by means of repetitive control method to investigate the asymptotic consensus.…”

Section: Adaptive Repetitive Consensus Framework For Multi-agent Systmentioning

confidence: 99%

“…To solve the consensus problem of the multi-agent systems with time-varying parametric uncertainties, much attention has been focused on the study of multi-agent systems by using adaptive iterative learning control [31][32][33]. In [31], a distributed adaptive iterative learning control was presented for the consensus problem of leader-following multi-agent systems, and the dynamic of each follower agent was nonlinear and with unknown time-varying parameter. The paper [32] also addressed an adaptive iterative learning control for multi-agent systems consensus tracking under repeatable control environment, where the agent dynamics were assumed to be inherently nonlinear with unknown time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The paper [32] also addressed an adaptive iterative learning control for multi-agent systems consensus tracking under repeatable control environment, where the agent dynamics were assumed to be inherently nonlinear with unknown time-varying parameters. Further, in [33], the authors extended the results in [31] to second-order multi-agent systems by means of an adaptive ILC approach.…”

Section: Introductionmentioning

confidence: 99%

“…Repetitive control is mainly used in continuous processes for tracking or rejecting periodic exogenous signals. The existing methods in [31][32][33] solved the coordination problems for the finite interval by using ILC method. How to investigate the asymptotic consensus for multi-agent systems with unknown time-varying parameters by means of repetitive control approach, which is still an open problem and remains unsolved.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed adaptive repetitive consensus control framework for uncertain nonlinear leader–follower multi-agent systems

2015

Journal of the Franklin Institute

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Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

Shen

2017

Adaptive Control & Signal

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This paper addresses the consensus problem of nonlinear multiagent system with state constraints. A novel γ-type barrier Lyapunov function is adopted to handle with the bounded constraints. The iterative learning control strategy is introduced to estimate the unknown parameter and basic control signal. Five control schemes are designed, in turn, to address the consensus problem comprehensively from both theoretical and practical viewpoints. These schemes include the original adaptive scheme, projection-based scheme, smooth function-based scheme and its alternative, and dead-zone-like scheme. The consensus convergence and constraints guarantee are strictly proved for each control scheme by using the barrier composite energy function approach. Illustrative simulations verify the theoretical analysis.

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Adaptive iterative learning control for consensus of multi‐agent systems

Cited by 81 publications

References 25 publications

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

Distributed adaptive repetitive consensus control framework for uncertain nonlinear leader–follower multi-agent systems

Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

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