ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform

Anuar Dorzhigulov, Batyrgali Bissengaliuly, B F Spencer, Jong Kim, Alex Pappachen James

Research output: Contribution to journalArticle

Abstract

The unmanned aerial vehicles can have complicated dynamics and kinematics that governs the flight of such multirotor devices. PID type controllers are one of the most popular approaches with Raspberry Pi 3 platform for stability of the flight. However, in dynamic environment they are limited in performance and response times. The autonomous tuning of the controller parameters according to the state of the environment with assistance of the adaptive neuro-fuzzy inference system is a well known approach. This paper provides implementation details and feasibility of such a controller with Raspberry Pi 3 platform for use in geological wireless sensing environments. The proposed neuro-fuzzy controller is developed for a Raspberry Pi 3 platform and tested on a physical quadrotor drone and compared to the conventional PID controller during flight.

Original languageEnglish (US)
Pages (from-to)435-445
Number of pages11
JournalAnalog Integrated Circuits and Signal Processing
Volume95
Issue number3
DOIs
StatePublished - Jun 1 2018

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Altitude control
Controllers
Fuzzy inference
Unmanned aerial vehicles (UAV)
Kinematics
Tuning
Drones

Keywords

  • ANFIS
  • Control
  • Drone stabilization
  • Neuro-fuzzy
  • Quadrotor

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Surfaces, Coatings and Films

Cite this

ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform. / Dorzhigulov, Anuar; Bissengaliuly, Batyrgali; Spencer, B F; Kim, Jong; James, Alex Pappachen.

In: Analog Integrated Circuits and Signal Processing, Vol. 95, No. 3, 01.06.2018, p. 435-445.

Research output: Contribution to journalArticle

Dorzhigulov, Anuar ; Bissengaliuly, Batyrgali ; Spencer, B F ; Kim, Jong ; James, Alex Pappachen. / ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform. In: Analog Integrated Circuits and Signal Processing. 2018 ; Vol. 95, No. 3. pp. 435-445.
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