Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

Abstract

In this paper, a novel method for aerodynamic model identification of a micro-air vehicle is proposed. The principal contribution is a technique of wind estimation that provides information about the existing wind during flight when no air-data sensors are available. The estimation technique employs multi-objective optimization algorithms that utilize identification errors to propose the wind-speed components that best fit the dynamic behavior observed. Once the wind speed is estimated, the flight experimentation data are corrected and utilized to perform an identification of the aircraft model parameters. A multi-objective optimization algorithm is also used, but with the objective of estimating the aerodynamic stability and control derivatives. Employing data from different flights offers the possibility of obtaining sets of models that form the Pareto fronts. Deciding which model best adjusts to the experiments performed (compromise model) will be the ultimate task of the control engineer.

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

“…In a future work, an additional processing task will be carried out, using the set of nodes, or control points ρ x (F) generated, to create a smoother path-and then the methodology will be tested under real flight conditions on an UAV model as in [64][65][66].…”

Motion Equations and Attitude Control in the Vertical Flight of a VTOL Bi-Rotor UAV

“…The four linear models are obtained by replacing the nonlinear equations of motion with their Taylor series approximation truncated to the first order with respect to the controlled variables and inputs. This linearization approach is well know and, frequently, it is defined as Small Perturbation Theory [27][28][29][30]. Once the set of equations have a linear structure and after replacing the model parameters by their prototype value, the following transfer functions are obtained: After calculating the transfer functions, the controllers parameters can be obtained either using classical techniques, such as the Root-Locus method, or more modern techniques based on optimisation with genetic algorithms.…”

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

“…A non-holonomic UAV [65] can perform flights in 3D Euclidean space. Nevertheless, to complete each movement sequence (horizontal and vertical), a set of UAV flight constraints must overcome.…”

“…Constraints It is important to mention that the characteristics of the UAV assumed in the experiments have been taken from [65], whose study has been carried out on a fixed wing UAV model kadett 2400, represented by six states (x, y, z, φ, θ, ψ), where the first three states define the position vector of the UAV's global coordinate system, located at the origin of its center of gravity. The last three are the Euler angles of roll, pitch and yaw respectively, which define the orientation of the UAV.…”