Fuzzy system identification for the dynamics of the AR.Drone quadcopter

Fendy Santoso, Matthew A. Garratt, Sreenatha G. Anavatti

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)

Abstract

In this work, we perform non-linear system identification by means of the Takagi-Sugeno (TS) fuzzy logic technique for the lateral, longitudinal, and vertical dynamics of the AR.Drone quadcopter. We derive the multi-input multi-output (MIMO) fuzzy models of the inner (attitude) loop control systems. Throughout extensive numerical simulations, we validate the accuracy of the proposed TS fuzzy models and compare them with respect to the performance of the model-based linear system identification technique. This paper serves as our preliminary study towards our long term goal in developing robust autopilot systems for the AR.Drone quadcopter using fuzzy models, which have many advantages over traditional system identification techniques (e.g. simplicity despite being non-linear, transparency, and the ability to accommodate ambiguity).

Original languageEnglish
Title of host publicationAustralasian Conference on Robotics and Automation 2016, ACRA 2016
PublisherAustralasian Robotics and Automation Association
Pages169-176
Number of pages8
ISBN (Electronic)9781634396080
Publication statusPublished - 2016
EventAustralasian Conference on Robotics and Automation 2016, ACRA 2016 - Brisbane, Australia
Duration: 05 Dec 201607 Dec 2016

Publication series

NameAustralasian Conference on Robotics and Automation, ACRA
Volume2016-December
ISSN (Print)1448-2053

Conference

ConferenceAustralasian Conference on Robotics and Automation 2016, ACRA 2016
Country/TerritoryAustralia
CityBrisbane
Period05/12/1607/12/16

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