Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

Abstract

This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR –Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.