Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Abstract

This research introduces a physics-based identification technique utilizing genetic algorithms. The primary objective is to derive a parametric matrix, denoted as A, describing the time-invariant linear model governing the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests that the genetic algorithm provided with the adequate fitness function could be an appealing alternative for aircraft model identification, even when limited data are available. Furthermore, in some cases, linearization using a genetic algorithm can be more efficient than classical methods.

Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach

“…A genetic algorithm, considering the transition matrix in its fitness function, has been successfully applied to identify the physics-based longitudinal dynamics of an aircraft. However, its main drawback is that it is an iterative process strongly dependent on the size of the search region for parameters [6].…”