Aerodynamic Parameter Estimation for Near-Stall Maneuver Using Neural Networks and Artificial Bee Colony Algorithm

Abstract

Accurate numerical values of aerodynamic parameters are important in aircraft design. The knowledge of stability and control aerodynamic parameters is essential to postulate high-fidelity control laws. The aerodynamic forces and moments are strong functions of the angle of attack (AOA), Reynolds number, and control surface deflections. Typically, conventional estimation techniques such as maximum likelihood (MLE) and least-squares (LS) principles facilitate the determination of these parameters. Unsteady aerodynamics may complicate the estimation of aerodynamic parameters at high AOA. Data-driven techniques employing neural networks provide an alternative for modeling the system behavior based on its observed state and control input variables. Nonlinearity increases because of flow separation at high AOA, which is close to stall. This paper explores the feasibility of employing a machine learning approach using neural networks to predict aircraft dynamics in a limited sense to identify aerodynamic characteristics. Integrating a neural network with the artificial bee colony (ABC) method facilitated the optimization of unknowns of the proposed aerodynamic model (AM). The proposed neural artificial bee colony (NABC) optimization approach estimated the longitudinal dynamics and stall properties for two experimental aircraft. Comparison of the estimates provided by the NABC approach with those of the standard MLE and neural Gauss–Newton (NGN) techniques established its efficacy. Furthermore, robust statistical analysis indicated that the proposed method provides a viable alternative for parameter estimation in nonlinear applications.