A data-driven online estimation method for aerodynamic parameter of high-speed aircraft considering strong nonlinearity and noise

Abstract

During large-scale maneuvers, high-speed aircrafts face more complex noise, greater overload and deformation, increasing the uncertainty and time-varying nonlinearity of the aerodynamics, which greatly influence the accuracy of aerodynamic parameters. An online modeling method is proposed by using least square support vector machine (LS-SVM) and unscented Kalman filter (UKF), to improve the estimation accuracy of aerodynamic parameters. Firstly, augmented state variables are designed with the dynamics and kinematics of the aircraft, in which the identified aerodynamic parameters are extended in the origin state variables. In this way, the identification of time-varying aerodynamic parameters is transformed into an online estimation of state variables. Then, an LS-SVM based online identification method is proposed to construct the coupling relationship of multiple parameters, and regarded as a data-driven observation equation with respect to augmented state variables. Furthermore, an UKF-based online update strategy integrating empirical knowledge and real-time information is proposed to improve the adaptability of aerodynamic parameters on data noise and external disturbances. The effectiveness of this method is verified through theoretical analysis and simulations.