Estimation of Angle of Attack with Artificial Neural Networks from Aerodynamic Model
Creators: name:  MUHIDDINOGLU ABDULLAH ALP 
name:   EFE MEHMET ONDER 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLIGHT DYNAMICS
GNC
AVIONICS
ESTIMATION
DYNAMICS RECONSTRUCTION

description: In this study, angle of attack estimation method is studied by using the measurable parameters of a projectile into an artificial neural network. For this study, a projectile with a set of specific aerodynamic characteristics is considered. The acceleration values of the projectile, whose aerodynamic properties are known, can be calculated depending on the angle of attack and other inputs. In this way, data sets, which are used in artificial neural network, are created. The time cost and performance that occurs with the expansion or reduction of data sets are the subject of the study. The search for the ideal artificial neural network structure for a successful prediction performance is another subject of the study. In this study, online or offline estimation of angle of attack with artificial neural network is explained. Equations \\eqref{fx1}-\\eqref{fz2} show that the accelerations are a function of $M$, $\\alpha$, $\\beta$, $\\omega$, $\\delta_a$, $\\delta_e$ and $\\delta_r$. Also aerodynamic coefficients depend on $M$, $\\alpha$, $\\beta$, $\\omega$, $\\delta_a$, $\\delta_e$ and $\\delta_r$. In order to estimate angle of attack, the relevant measurable parameters are taken as input. An artificial neural network structure is planned with accelerometer, gyro and aerodynamic surface deflection angles as inputs. There is no known instrument for measuring the angle of attack on projectiles. Known theoretical solutions make use of integrals of auxiliary instruments in practice, which accumulates errors and progressively degrades performance towards the end of the flight. Therefore, in this study, it is investigated whether it is possible to make a measurement using only the outputs of the instruments and the aerodynamic database. According to the study, the \"Equespaced Distribution (ED)\" performance is considered suitable for the estimation of the angle of attack with artificial neural networks. However, training ANN with ED is costly. In the face of this problem, training ANN with \"Normal Distribution (ND)\" is recommended as an alternative and a comparison is made in this regard. In the first study, an ANN with 1,000 data is trained with ND, but sufficient performance is not provided. Then, ANN with 10,000 data is trained with ND, the performance has increased, but it is not at the desired level. Finally, with 10,000 data sets ANN, the training is repeated for more layers (three layers with ten neurons) and the system performance reaches a sufficient level for prediction. It has been observed that the performance is improved as the data frequency increases. However, for the time cost incurred, sampled data with normal distribution are tried and the estimation performance is found at the desired level. In the full presentation of the study, how the test parameters are selected, why the input and output parameters of the artificial neural network are correlated, and the comparison of online performance in a simulation flight with conventional methods will also be presented.