A small fixed-wing UAV system identification using metaheuristics

A novel method for system identification of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs) using a metaheuristics (MHs) approach is proposed. This investigation splits the complex aerodynamic model of UAV into longitudinal and lateral dynamics sub-systems. The system identification optimisat...

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Bibliographic Details
Published inCogent engineering Vol. 9; no. 1
Main Authors Nonut, Apiwat, Kanokmedhakul, Yodsadej, Bureerat, Sujin, Kumar, Sumit, Tejani, Ghanshyam G., Artrit, Pramin, Yıldız, Ali Rıza, Pholdee, Nantiwat
Format Journal Article
LanguageEnglish
Published Abingdon Cogent 31.12.2022
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
aerodynamic
Aerodynamic stability
Aircraft
Algorithms
Aviation
computational fluid dynamics
Data collection
Design
Dynamic models
Error analysis
Experiments
Fixed wings
Fluid dynamics
Fuzzy sets
Heuristic methods
Identification
Kalman filters
Mathematical models
Mechanical engineering
optimisation
Optimization
Parameter estimation
R&D
Rank tests
Research & development
Stability derivatives
Statistical analysis
System identification
unmanned aerial vehicle
Unmanned aerial vehicles
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Summary:A novel method for system identification of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs) using a metaheuristics (MHs) approach is proposed. This investigation splits the complex aerodynamic model of UAV into longitudinal and lateral dynamics sub-systems. The system identification optimisation problem is proposed to find the UAV aerodynamic and stability derivatives by minimizing the R-squared error between the measurement data and the flight dynamic model. Thirteen popular optimisation algorithms are applied for solving the proposed UAV system identification optimisation problem while each algorithm is tested for 10 independent optimisation runs. By performing the Freidman's rank test, statistical analysis of the experiment work was carried out while, based on the fitness value, each algorithm is ranked. The outcomes demonstrate the dominance of the L-SHADE algorithm, with mean R-square errors of 0.5465 and 0.0487 for longitudinal and lateral dynamics, respectively. It is considered superior to the other algorithms for this system identification problem.
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ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2022.2114196