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

57 Scopus citations


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
Issue number5
StatePublished - May 2021

Bibliographical note

Funding Information:
Dr. Zhang is a recipient of the National Postdoctoral Innovative Talent Scholars of China, and has received the National Excellent Doctoral Dissertation Award in the field of intelligent transportation.

Funding Information:
Manuscript received November 18, 2019; revised April 17, 2020; accepted June 25, 2020. Date of publication July 21, 2020; date of current version April 15, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 51909018; in part by the Natural Science Foundation of Liaoning Province under Grant 20170520189 and Grant 20180520039; in part by the Science and Technology Innovation Foundation of Dalian City under Grant 2019J12GX026; in part by the National Postdoctoral Program for Innovative Talents under Grant BX201600103; and in part by the Fundamental Research Funds for the Central Universities of China under Grant 3132020124 and Grant 3132019306. This article was recommended by Associate Editor Y. Pan. (Corresponding author: Guoqing Zhang.) Guoqing Zhang and Shengjia Chu are with the Navigation College, Dalian Maritime University, Dalian 116026, China (e-mail:;

Publisher Copyright:
© 2013 IEEE.


  • 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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