Event-triggered adaptive neural networks tracking control for incommensurate fractional-order nonlinear systems with external disturbance

The event-triggered adaptive neural networks (NNs) tracking control issue is considered in this paper for a type of incommensurate fractional-order systems (IFOSs) with disturbance. Unlike the existing works for IFOSs, the hypothesis of disturbance-like function and the frequency distributed model a...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2023-10, Vol.554, p.126586, Article 126586
Main Authors: Cao, Boqiang, Nie, Xiaobing, Cao, Jinde
Format: Article
Language:English
Subjects:
Adaptive tracking control
Event-triggered mechanism
External disturbance
Incommensurate fractional-order system
Neural networks approximation
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Summary:The event-triggered adaptive neural networks (NNs) tracking control issue is considered in this paper for a type of incommensurate fractional-order systems (IFOSs) with disturbance. Unlike the existing works for IFOSs, the hypothesis of disturbance-like function and the frequency distributed model are avoided in this study by utilizing the continuity of fractional-order derivative (FOD). Then, based on the radial basis function (RBF) NNs and backstepping method, the adaptive control scheme is proposed to ensure that the reference signal can be tracked by the system output, where the derivative order of adaptive laws does not depend on the one of IFOS. Additionally, to further reduce the burden of communication network, an exponentially convergent term is introduced into traditional dynamic event-triggered mechanism (ETM) and the Zeno behavior is avoided. Finally, two illustrative examples are exhibited to verify the performance of designed control scheme.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2023.126586