A small fixed-wing UAV system identification using metaheuristics

Abstract

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.