Approaches to real-time adaptive prediction of loss-of- control margins with visual cue feedback

Abstract

Loss-of-Control events continue to be the most significant contributing factor to air accidents worldwide. Predictive systems capable of warning pilots of impending entry into a control loss event have the potential to promote safer flight. Towards this goal, two prediction models are developed which adaptively calculate an aircraft's proximity to its loss-of-control margins given current-time pilot inputs. The first model utilizes analytical expressions for the time domain response of the 1st and 2nd order aircraft modes to predict critical control deflections, while the second model utilizes a linear quadratic tracker to predict critical control trajectories. Adaptive parameter estimation is implemented to facilitate real-time modeling error identification, to account for non-linearities, failures, or uncertainties in the aircraft model. Two head-up advisory displays are developed to present the predicted control limits and loss-of-control margins to the pilot, providing pre-emptive warning if impending entry into a control loss event is predicted to occur within the future-time prediction window. The predictive architecture is applied in simulation to the short period, roll, and dutch roll modes of a light business jet, and results demonstrate that the models are able to successfully predict, for various initial conditions and for various failure scenarios, the critical control limits that should not be exceeded in order to avoid entry into a control loss situation.