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.
Highlights
The most generic definition of parameter estimation is the method of obtaining the most probable values of the aerodynamic derivatives, which are used to define the system itself
The genetic algorithm optimized output error method is employed for the estimation of the longitudinal aerodynamic parameters of the aircraft
The proposed method is applied on simulated flight data (Without noise and with random noise) and real flight data
Summary
The most generic definition of parameter estimation is the method of obtaining the most probable values of the aerodynamic derivatives, which are used to define the system itself. The parameter estimation is the subset of System Identification, which is a broader field including a comprehensive understanding of the underlying physical principle and determination of unknown stability and control derivatives [1,2,3,4,5,6]. The System Identification comprises of the postulation of all feasible mathematical models, choosing the most suitable model amongst all models, and the estimation of unknown parameters. The most critical portion of model postulation is the determination of a model for the estimation of aerodynamic forces, as they cannot be obtained directly. The scope of the mathematical model for the determination of aerodynamic forces is to devise suitable connections of forces (X, Y, Z) and moments (L, M, N) along the three axes in terms of translational motion variables (u, v, w), rate of rotation (p, q, r) and control surface deflections along the three axes [1]
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