Virtual sensing of aircraft extreme parameters based on parameter spatial-temporal correlation

Aircraft wing parameters are a key variable that reflects the status of the aircraft and evaluates the operational safety of the aircraft. Nevertheless, limited by the wing structure and harsh operating environment, it is difficult to measure wing parameters by deploying sensors and other methods. Simultaneously, the alterations in wing configuration and the intricate operating environment lead to dynamic variations in the coupling relationship between wing parameters, so it is difficult to use a single output model to fit the parameter changes. Therefore, this paper proposes a wing parameter uncertainty modeling method based on parameter spatial-temporal coupling. This paper establishes a mapping model that links measurable characteristics (such as airspeed, Aileron angle, etc.) with unmeasurable parameters (such as Aileron, etc.) in order to enable the measurement perception of these parameters. Furthermore, this paper employs Bayesian inference to assess the level of uncertainty in the model's output. Ultimately, this paper employs the airplane simulation data set to validate the suggested approach.