Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach
Joint Authors
El gmili, Nada
Mjahed, Mostafa
El kari, Abdeljalil
Ayad, Hassan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS).
The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS.
To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor.
After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods.
Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.
American Psychological Association (APA)
El gmili, Nada& Mjahed, Mostafa& El kari, Abdeljalil& Ayad, Hassan. 2019. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129644
Modern Language Association (MLA)
El gmili, Nada…[et al.]. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1129644
American Medical Association (AMA)
El gmili, Nada& Mjahed, Mostafa& El kari, Abdeljalil& Ayad, Hassan. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129644
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129644