Title: Hybrid ANFIS-ant colony based optimisation for quadrotor trajectory tracking control

Authors: Boumediene Selma; Samira Chouraqui; Hassane Abouaïssa

Addresses: Département d'Informatique, Université des sciences et de la technologie d'Oran USTO'MB, Oran, 31000, Algeria ' Département d'Informatique, Université des sciences et de la technologie d'Oran USTO'MB, Oran, 31000, Algeria ' Univ. Artois, EA 3926, Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Béthune, F-62400, France

Abstract: This study proposes a robust, accurate and intelligent control for trajectory tracking of a three degree of freedom quadrotor unmanned aerial vehicle (UAV). The controller is based on adaptive-network-based fuzzy inference system (ANFIS) and ant colony optimisation (ACO) algorithm. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the reference trajectory of the quadrotor in 2-D vertical plane. The ACO algorithm provides an automatic adjustment of ANFIS parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed ANFIS controller tuned by ACO, it is compared with ANFIS and proportional-integral-derivative (PID) controllers. As expected, the hybrid ANFIS-ACO controller gives more satisfactory results than the other methods already developed in the same study.

Keywords: UAV; unmanned aerial vehicle; intelligent control; ANFIS; adaptive-network-based fuzzy inference system; ACO; ant colony optimisation.

DOI: 10.1504/IJMIC.2020.10031054

International Journal of Modelling, Identification and Control, 2020 Vol.34 No.1, pp.13 - 25

Received: 11 Sep 2019
Accepted: 15 Jan 2020

Published online: 04 Aug 2020 *

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