Event-Triggered Saturated Adaptive Iterative Learning Control of Nonlinear Fractional-Order Multi-Agent Systems

This paper studies the event-triggered saturated adaptive iterative learning control (ETSAILC) problem for the fractional-order multi-agent systems (FOMASs) subject to the local Lipschitz nonlinearities and the input saturation. First, a event-triggered mechanism is proposed to ensure that events oc...

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Vydané v:	IEEE access Ročník 11; s. 1
Hlavní autori:	Liu, Yusen, Wang, Liming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive control Closed loops Consensus protocol Convergence Event-triggered control fractional-order multi-agent systems input saturation Iterative learning control Learning local Lipschitz nonlinearities Multi-agent systems Multiagent systems Nonlinear control Nonlinearity perfect tracking of consensus Process control Stability analysis Topology
ISSN:	2169-3536, 2169-3536
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Abstract	This paper studies the event-triggered saturated adaptive iterative learning control (ETSAILC) problem for the fractional-order multi-agent systems (FOMASs) subject to the local Lipschitz nonlinearities and the input saturation. First, a event-triggered mechanism is proposed to ensure that events occur along an iteration axis, and all follower agents synchronously broadcast their states at each triggering iteration step, receive and restore the information from their neighbors. Next, the ETSAILC protocol is designed and the event-triggered condition is presented. Then, the composite energy function (CEF) with the form of fractional-order integral is constructed and is utilized for the convergence analysis of iterative learning process, thereby the boundedness of closed-loop signals is proved and a sufficient condition guaranteeing the perfect consensus is obtained. It is for the first time to extend the CEF method into the field of fractional-order systems. Finally, compared with the existing consensus protocols, simulations demonstrate that the proposed controller can effectively handle the local Lipschitz nonlinearities and the input saturation and obtain the perfect control of consensus. Meanwhile, the number of control input update and the amount of transmitted information are obviously reduced.
AbstractList	This paper studies the event-triggered saturated adaptive iterative learning control (ETSAILC) problem for the fractional-order multi-agent systems (FOMASs) subject to the local Lipschitz nonlinearities and the input saturation. First, a event-triggered mechanism is proposed to ensure that events occur along an iteration axis, and all follower agents synchronously broadcast their states at each triggering iteration step, receive and restore the information from their neighbors. Next, the ETSAILC protocol is designed and the event-triggered condition is presented. Then, the composite energy function (CEF) with the form of fractional-order integral is constructed and is utilized for the convergence analysis of iterative learning process, thereby the boundedness of closed-loop signals is proved and a sufficient condition guaranteeing the perfect consensus is obtained. It is for the first time to extend the CEF method into the field of fractional-order systems. Finally, compared with the existing consensus protocols, simulations demonstrate that the proposed controller can effectively handle the local Lipschitz nonlinearities and the input saturation and obtain the perfect control of consensus. Meanwhile, the number of control input update and the amount of transmitted information are obviously reduced.
Author	Wang, Liming Liu, Yusen
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SubjectTerms	Adaptive control Closed loops Consensus protocol Convergence Event-triggered control fractional-order multi-agent systems input saturation Iterative learning control Learning local Lipschitz nonlinearities Multi-agent systems Multiagent systems Nonlinear control Nonlinearity perfect tracking of consensus Process control Stability analysis Topology
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Title	Event-Triggered Saturated Adaptive Iterative Learning Control of Nonlinear Fractional-Order Multi-Agent Systems
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