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SCIENCE CHINA Information Sciences, Volume 63, Issue 5: 150205(2020) https://doi.org/10.1007/s11432-019-2679-5

Event-triggered neural network control of autonomous surface vehicles over wireless network

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  • ReceivedMay 10, 2019
  • AcceptedSep 16, 2019
  • PublishedMar 16, 2020

Abstract

In this paper, an event-triggered neural network control method is proposed for autonomous surface vehicles subject to uncertainties and input constraints over wireless network.An event-triggered mechanism with three logic rules is employed to determine the wireless data transmission of states and control inputs.An event-driven neural network is applied to approximate the uncertainties using aperiodic sampled states.In addition, a predictor is employed to update the weights of neural network.An event-based bounded kinetic control law is applied to address the actuator constraints.The advantage of the proposed event-triggered neural network control approach is that the network traffic can be reduced while guaranteeing system stability and speed following performance.The closed-loop control system is proved to be input-to-state stable via cascade theory.The Zeno behavior can be avoided via the proposed event-triggered neural network control approach.A simulation example is provided to demonstrate the effectiveness of the proposed event-triggered neural network control approach for autonomous surface vehicles.


Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61673081, 51979020, 51909021, 51579023), Training Program for High-level Technical Talent in Transportation Industry (Grant No. 2018-030), Innovative Talents in Universities of Liaoning Province (Grant No. LR2017014), Science and Technology Fund for Distinguished Young Scholars of Dalian (Grant No. 2018RJ08), Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology (Grant No. JCKYS2019604SXJQR-01), Fundamental Research Funds for the Central Universities (Grant No. 3132019319), and China Postdoctoral Science Foundation (Grant No. 2019M650086).


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