Adaptive Iterative Learning Control for Nonsquare Nonlinear Multi-Agent Systems Subject to Unknown Fading Channels

This paper investigates the consensus tracking control problem of nonsquare nonlinear multi-agent systems (MASs) subject to unknown fading channels. In order to compensate for the impact of the unknown fading channels, a learning-based estimation approach is proposed to estimate the unknown fading g...

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Vydáno v:Nonlinear dynamics Ročník 113; číslo 17; s. 23083 - 23102
Hlavní autoři: Zeng, Dezheng, Li, Xiao-Dong, Li, Xuefang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.09.2025
Springer Nature B.V
Témata:
Algorithms
Applications of Nonlinear Dynamics and Chaos Theory
Autonomous vehicles
Channels
Classical Mechanics
Communication channels
Control
Control systems
Controllers
Dynamical Systems
Fading
Learning
Multiagent systems
Network topologies
Nonlinear control
Nonlinear systems
Physics
Physics and Astronomy
Statistical Physics and Dynamical Systems
Tracking control
Vibration
Adaptive iterative learning control
Consensus tracking
Nonsquare
Unknown fading channels
Multi-agent systems
ISSN:0924-090X, 1573-269X
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Shrnutí:This paper investigates the consensus tracking control problem of nonsquare nonlinear multi-agent systems (MASs) subject to unknown fading channels. In order to compensate for the impact of the unknown fading channels, a learning-based estimation approach is proposed to estimate the unknown fading gain, based on which a novel adaptive iterative learning control (AILC) scheme is then developed to achieve the consensus tracking task in presence of system uncertainties and unknown fading channels. In contrast to the existing AILC algorithms, the proposed AILC scheme is applicable to nonsquare systems without using the invertibility property of the control gain matrices. In addition, the convergence analysis of the proposed AILC strategy is carried out with the aid of the composite energy function (CEF) method. The proposed AILC scheme is also implemented to consensus tracking of autonomous vehicles, where the effectiveness is verified.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-025-11320-y