Real-time landing distance prediction for aircraft based on fuzzy inference

A hybrid landing distance prediction algorithm based on fuzzy inference is suggested to overcome the shortcomings of current approaches in real-time performance and handling nonlinear dynamics in order to successfully prevent runway excursion incidents during aircraft landings. The method integrates multiple prediction strategies based on the three distinct dynamic phases of the landing process: during the glide phase, a ground speed vector mapping method is used for prediction; during the rollout phase, a trajectory analysis method is employed; and during the flare phase, which involves significant state changes and is difficult to model accurately, a Mamdani-type fuzzy inference method is applied. A high-fidelity simulation platform for multi-condition validation is also proposed. With a prediction inaccuracy of fewer than 35 meters during the flare phase and a computation time of less than 3 milliseconds per calculation, this platform can deliver real-time landing distance forecasts in turbulent wind environments. The proposed method meets real-time decision-making requirements and offers a feasible engineering strategy to enhance landing safety margins.