A Joint Online Estimation Method for Aircraft Aerodynamic Parameters and Thrust Deviation

Direct measurement of engine thrust during aircraft flight remains challenging. Currently, engineering methods yield only coarse approximations of thrust during flight. This limitation significantly impedes aerodynamic parameter identification from powered flight data, particularly undermining the credibility and accuracy of the identification results of aerodynamic force coefficients. To address these challenges inherent in aerodynamic parameter identification from powered flight data, this study proposes a novel joint online estimation method capable of simultaneously estimating system states, unknown aerodynamic parameters, and engine thrust. The algorithm integrates Kalman filters with computationally efficient recursive least squares (RLS) estimators to perform sequential estimation of flight data. This structure provides real‐time access to unmeasurable engine thrust and enhancement of the estimation precision of aerodynamic parameters. The effectiveness of the proposed algorithm was rigorously validated and assessed using both simulation and flight test data from the CAE‐AVM benchmark aircraft model. The method successfully generated valid estimates of engine thrust and aerodynamic parameters from both datasets and exhibited superiority over the EKF and MMAE algorithms. Specifically, for flight test phases including climb, cruise, and descent, the maximum root mean square relative error (RMSE) for thrust estimates was found to be only 17.36%. These results demonstrate the high estimation accuracy of the proposed joint estimation method for both simulation and flight test data and validate its high effectiveness for the identification and processing of aircraft‐powered flight data.