Aerodynamic parameter estimation provides an effective way for aerospace system modeling using measured data from flight tests, especially for the purpose of developing elaborate simulation environments and designing ...Aerodynamic parameter estimation provides an effective way for aerospace system modeling using measured data from flight tests, especially for the purpose of developing elaborate simulation environments and designing control systems of unmanned aerial vehicle （UAV） with short design cycles and reduced cost. However, parameter identification of airplane dynamics by nonlinear mod- els is complicated because of the noisy and biased sensor measurements. Using linear models for system identification is an alternative way if the fidelity can be guaranteed, as control design procedures are better established in linear systems. This paper considers the application and comparison of linear as well as nonlinear aerodynamic parameter estimation approaches of an UAV using unscented Kalman filter （UKF）. It also highlights the degree of deterioration of the linear model in the UKF identification process. The results show that both the linear and nonlinear methodologies can accurately estimate the control system design. Furthermore, considering loss of accuracy to be negligible, the linear model can be employed for control design of the UAV as presented here.展开更多
Aerodynamic modeling and parameter estimation from quick accesses recorder （QAR） data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane....Aerodynamic modeling and parameter estimation from quick accesses recorder （QAR） data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane. It is also an essential content of flight accident analysis. The related techniques are developed in the present paper, including the geometric method for angle of attack and sideslip angle estimation, the extended Kalman filter associated with modified Bryson-Frazier smoother （EKF-MBF） method for aerodynamic coefficient identification, the radial basis function （RBF） neural network method for aerodynamic mod- eling, and the Delta method for stability/control derivative estimation. As an application example, the QAR data of a civil air- plane approaching a high-altitude airport are processed and the aerodynamic coefficient and derivative estimates are obtained. The estimation results are reasonable, which shows that the developed techniques are feasible. The causes for the distribution of aerodynamic derivative estimates are analyzed. Accordingly, several measures to improve estimation accuracy are put forward.展开更多
基金Supported by the Engineering and Physical Sciences Research Council(EPSRC),UK(EP/F037570/1)
文摘Aerodynamic parameter estimation provides an effective way for aerospace system modeling using measured data from flight tests, especially for the purpose of developing elaborate simulation environments and designing control systems of unmanned aerial vehicle （UAV） with short design cycles and reduced cost. However, parameter identification of airplane dynamics by nonlinear mod- els is complicated because of the noisy and biased sensor measurements. Using linear models for system identification is an alternative way if the fidelity can be guaranteed, as control design procedures are better established in linear systems. This paper considers the application and comparison of linear as well as nonlinear aerodynamic parameter estimation approaches of an UAV using unscented Kalman filter （UKF）. It also highlights the degree of deterioration of the linear model in the UKF identification process. The results show that both the linear and nonlinear methodologies can accurately estimate the control system design. Furthermore, considering loss of accuracy to be negligible, the linear model can be employed for control design of the UAV as presented here.
基金National Natural Science Foundation of China(60832012)
文摘Aerodynamic modeling and parameter estimation from quick accesses recorder （QAR） data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane. It is also an essential content of flight accident analysis. The related techniques are developed in the present paper, including the geometric method for angle of attack and sideslip angle estimation, the extended Kalman filter associated with modified Bryson-Frazier smoother （EKF-MBF） method for aerodynamic coefficient identification, the radial basis function （RBF） neural network method for aerodynamic mod- eling, and the Delta method for stability/control derivative estimation. As an application example, the QAR data of a civil air- plane approaching a high-altitude airport are processed and the aerodynamic coefficient and derivative estimates are obtained. The estimation results are reasonable, which shows that the developed techniques are feasible. The causes for the distribution of aerodynamic derivative estimates are analyzed. Accordingly, several measures to improve estimation accuracy are put forward.
