Enhanced robust output tracking of nonlinear systems with dynamic event-triggering using neural network-based method

dc.contributor.authorChen, Z.
dc.contributor.authorZhang, H.
dc.contributor.authorShi, P.
dc.contributor.authorHuang, Y.
dc.contributor.authorAssawinchaichote, W.
dc.date.issued2024
dc.description.abstractThis paper investigates the problem of robust tracking control for a class of nonlinear systems using a novel three-layer fully connected feedforward neural network controller. The weights of the hidden and output layers of this neural network controller are obtained by solving linear matrix inequalities, while the weights of the input and hidden layers are optimized using a genetic algorithm. Notably, the fitness function for training the genetic algorithm is the square of the difference between the reference signal and the controlled system output signal within the whole period. Moreover, considering external disturbances and time delays of networks, a novel Lyapunov- Krasovskii functional is constructed to derive sufficient conditions for the asymptotic stability with an H∞ performance of the nonlinear system. Furthermore, to conserve communication resources and reduce the computational load of the neural network controller, a dynamic event-triggered scheme with a nonnegative intermediate variable is implemented. Finally, the tracking effect of the nonlinear system on two types of reference signals is tested on an inverted pendulum model to illustrate and validate the effectiveness of the proposed controller.
dc.description.statementofresponsibilityZixian Chen, Huiyan Zhang, Peng Shi, Yu Huang, Wudhichai Assawinchaichote
dc.identifier.citationNonlinear Dynamics, 2024; 113(1):547-566
dc.identifier.doi10.1007/s11071-024-10125-9
dc.identifier.issn0924-090X
dc.identifier.issn1573-269X
dc.identifier.orcidShi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]
dc.identifier.urihttps://hdl.handle.net/2440/146209
dc.language.isoen
dc.publisherSpringer
dc.relation.granthttp://purl.org/au-research/grants/arc/DP240101140
dc.rights© The Author(s), under exclusive licence to Springer Nature B.V. 2024
dc.source.urihttps://doi.org/10.1007/s11071-024-10125-9
dc.subjectnonlinear systems; dynamic event-triggered scheme; robust tracking control; neural network controller
dc.titleEnhanced robust output tracking of nonlinear systems with dynamic event-triggering using neural network-based method
dc.typeJournal article
pubs.publication-statusPublished

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