Robust maneuvering envelope estimation based on reachability analysis in an optimal control formulation
This paper discusses an algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. Starting with an optimal control formulation, the optimization problem can be rewritten as a Hamilton-Jacobi-Bellman equation. This equation can be solved by level set methods. This approach has been applied on an aircraft example involving structural airframe damage. Monte Carlo validation tests have confirmed that this approach is successful in estimating the safe maneuvering envelope for damaged aircraft.
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.
Online Aerodynamic Model Structure Selection and Parameter Estimation for Fault Tolerant Control
This paper describes a new recursive algorithm for the approximation of time varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called Adaptive Recursive Orthogonal Least Squares (AROLS), and is an extension and modification of the classical Recursive Orthogonal Least Squares (ROLS). This algorithm is considered to be particularly useful for indirect fault tolerant flight control, making use of model based adaptive control routines. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on some simulation data sets.