Composite Neural Learning Fault-Tolerant Control for Underactuated Vehicles with Event-Triggered Input

Guoqing Zhang, Shengjia Chu, Xu Jin, Weidong Zhang

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.

Original languageEnglish
Article number9145820
Pages (from-to)2327-2338
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume51
Issue number5
DOIs
StatePublished - May 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Composite neural learning
  • event-triggered control
  • fault-tolerant control
  • path following
  • underactuated ships

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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