Fault tolerant control for a class of nonlinear system based on active disturbance rejection control and rbf neural networks

Lushan Zhou, Liling Ma, Junzheng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

In this paper, a fault tolerant control method based on active disturbance rejection control (ADRC) and radial basis function neural network (RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults, components faults and sensor faults. The proposed method does not rely on the plant model. By regarding the faults and plant uncertainties as the disturbance, through the observation of extended state observer and the compensation of feedback control signal, this method achieves the fault tolerance control of the plant with component fault and actuator fault. For sensor faults, in this work, radial basis function neural network is applied to estimate the real output of the system. Then this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of sensor. Finally, the effectiveness of the proposed method is validated by the simulation results of the three-tank system.

Original languageEnglish
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages7321-7326
Number of pages6
ISBN (Electronic)9789881563934
DOIs
Publication statusPublished - 7 Sept 2017
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: 26 Jul 201728 Jul 2017

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference36th Chinese Control Conference, CCC 2017
Country/TerritoryChina
CityDalian
Period26/07/1728/07/17

Keywords

  • Fault tolerant control
  • active disturbance rejection control
  • radial basis function neural network

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