Online learning and inference based flight envelope estimation for aircraft loss-of-control prevention

Aircraft loss-of-control (LOC) is the major contributing factor to fatal accidents and is characterised by the manoeuvring of aircraft beyond the allowable flight envelopes. This paper proposes an online learning and inference based method for aircraft flight envelope estimation in order to prevent aircraft LOC. The lift and drag coefficients of the aircraft are identified online using an extended Kalman filter and the aircraft flight dynamics. A Gaussian process regression model then learns and infers the up-to-date form of the lift curve from both prior knowledge and the identification data. The extremum of the inferred lift curve, including the maximum lift coefficient and the critical angle of attack are used to compute the flight envelope estimate of the aircraft. Numerical simulation on the NASA generic transportation model (GTM) shows that the proposed method can effectively estimate the aircraft lift and drag coefficients, and by using the extremum on the up-to-date lift curve inferred, return the flight envelope under a wingtip impairment condition.