Work

Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

Year: 2019

Type: article

Abstract: 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.

Source: Computational Intelligence and Neuroscience

DOI: 10.1155/2019/8925165

Authors: Nada El gmili, Mostafa Mjahed, Abdeljalil El kari, Hassan Ayad

Institutions: Cadi Ayyad University, École Royale de l'Air de Marrakech

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

OpenAlex ID: W2963483841