Collective point-to-point iterative learning control of multi-agent system with switched reference

Zhao, Xi; Tuo, Jianyong; Wang, Youqing

doi:10.1016/j.jfranklin.2023.07.033

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…This ensures that formation members achieve their targets within predefined time frames. Additionally, ILC adapts to changing communication topologies and responds to external stimuli, enhancing both the stability and efficiency of information control [7,8]. Compared to other control methods, ILC offers superior precision and reliability, making it a preferred solution for multi-agent system formation control.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Formation Control via Input Sharing for Fractional-Order Singular Multi-Agent Systems with Local Lipschitz Nonlinearity

Wang,

et al. 2024

Fractal Fract

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For a class of fractional-order singular multi-agent systems (FOSMASs) with local Lipschitz nonlinearity, this paper proposes a closed-loop Dα-type iterative learning formation control law via input sharing to achieve the stable formation of FOSMASs in a finite time. Firstly, the formation control issue of FOSMASs with local Lipschitz nonlinearity under the fixed communication topology (FCT) is transformed into the consensus tracking control scenario. Secondly, by virtue of utilizing the characteristics of fractional calculus and the generalized Gronwall inequality, sufficient conditions for the convergence of formation error are given. Then, drawing upon the FCT, the iteration-varying switching communication topology is considered and examined. Ultimately, the validity of the Dα-type learning method is showcased through two numerical cases.

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Section: Introductionmentioning

confidence: 99%

Iterative Learning Formation Control via Input Sharing for Fractional-Order Singular Multi-Agent Systems with Local Lipschitz Nonlinearity

Wang,

et al. 2024

Fractal Fract

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Event-Triggered Iterative Learning Control for First-Order MIMO Discrete Nonlinear Hyperbolic Distributed Parameter Systems

Wang,

Dai

2024

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)

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Iterative Learning Control Design for Iteration-Varying State-of-Charge Profiles of Electric Vehicle Batteries

Nguyen

2025

IEEE Trans. Syst. Man Cybern, Syst.

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Collective point-to-point iterative learning control of multi-agent system with switched reference

Cited by 3 publications

References 37 publications

Iterative Learning Formation Control via Input Sharing for Fractional-Order Singular Multi-Agent Systems with Local Lipschitz Nonlinearity

Iterative Learning Formation Control via Input Sharing for Fractional-Order Singular Multi-Agent Systems with Local Lipschitz Nonlinearity

Event-Triggered Iterative Learning Control for First-Order MIMO Discrete Nonlinear Hyperbolic Distributed Parameter Systems

Iterative Learning Control Design for Iteration-Varying State-of-Charge Profiles of Electric Vehicle Batteries

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