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.

The current work offers the determination of longitudinal aerodynamic derivatives during flight manoeuver at angles of attack near the stall. The flight manoeuver near stall is highly non-linear in nature due to separated flow at such elevated angles of attack. Kirchoff's model for Quasi-Steady Stall Modelling (QSSM) is employed to represent the non-linear nature of aerodynamics during flight manoeuver at elevated angles of attack close to the stall. The Genetic Algorithm (GA) optimized output error method is utilized for estimating the parameters specific to stall charactertistics and longitudinal aerodynamics of the ATTAS(Advanced Technologies Testing Aircraft System) aircraft. The comparative evaluation of the parameter estimates with the estimates obtained by using Maximum Likelihood technique is employed to assess the efficacy of the proposed method for highly non-linear applications. The comparative assessment of the estimates along with robust statistical analysis evidence that the proposed method can be a suitable parameter estimation alternative method for non-linear applications.

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