Decoupling design of rolling aircraft using adaptive neuro-fuzzy inference systems based on T-S model

Jie Guo*, Qian Xu, Jingnan Zhang, Shengjing Tang

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Considering the coupling problem which is adverse to the control performance of rolling aircraft system, a dynamic decoupling method is proposed combining the feedforward compensation decoupling method and the adaptive neuro-fuzzy inference system. First, the decoupling compensation principle is deduced under the feedforward decoupling framework. And then, the adaptive neuro-fuzzy inference system based on Takagi-Sugeno model is adopted to approximate the decoupling compensation principle. At last, the trained adaptive neuro-fuzzy inference system is used as the dynamic decoupling compensator in series form. A design example of actuator link in rolling aircraft is employed to verify the validity of this method. The simulation results indicate that this method can obtain good decoupling performance in both dynamic and steady responses when it is incapable to get the accurate system model.

Original languageEnglish
Title of host publicationProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
Pages339-343
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012 - Chongqing, China
Duration: 29 May 201231 May 2012

Publication series

NameProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

Conference

Conference2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
Country/TerritoryChina
CityChongqing
Period29/05/1231/05/12

Keywords

  • decoupling
  • feedforward compensation
  • fuzzy inference system
  • rolling aircraft

Fingerprint

Dive into the research topics of 'Decoupling design of rolling aircraft using adaptive neuro-fuzzy inference systems based on T-S model'. Together they form a unique fingerprint.

Cite this