A Method for Estimating Control Failure Effects for Aerodynamic Vehicle Trajectory Retargeting

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Advanced guidance and control utilizes control reconflguration, guidance adaptation, trajectory reshaping, and an on-line mission/abort planner to achieve the best possible mission given that the vehicle’s performance has been degraded by some sort of failure. Potential failures include actuator loss (locked, ∞oating), engine out, damage to the vehicle, aerodynamic mismodelling, and so on. The trend towards the development of autonomous vehicles has placed more emphasis on requiring trajectory retargeting/reshaping algorithms. One of the main di‐culties in trajectory retargeting is predicting the efiects of failures at future ∞ight conditions. In this work, a method is presented that can generate critical information that is required to perform on-line trajectory retargeting. In particular, a method for estimating failure induced constraints, for failures involving locked or ∞oating control efiectors, is presented which accounts for 6 degree-of-freedom (DOF) effects upon the reduced order models that are used by trajectory generation algorithms. These constraints on the vehicle are not constant and can vary widely over difierent ∞ight conditions. This means that one cannot assume that a set of constraints estimated at a one ∞ight condition will be valid at any other ∞ight condition. This phenomenon limits the class of failures for which one can estimate constraints or 6 DOF efiects on reduced order models, using only the original aerodynamic database. Efiector failures such as locked or ∞oating surfaces are a class of failures whose efiects can be estimated over a wide range of operating conditions. This is because the aerodynamic database for the vehicle does not change as a result of such a failure. The efiects of locked or ∞oating surfaces can be estimated at all ∞ight conditions for which the original aerodynamic database is valid. Online aerodynamic database estimation techniques have not been developed and sensing and identiflcation of outer mold line changes compounds the problem. In order to generate a practical on-line trajectory retargeting algorithm, vehicle constraints must be estimated at future ∞ight conditions and in this work we limit ourselves to dealing with efiector failures that can be directly sensed. In this paper, the constraint estimation issue is discussed and brought to the forefront with the use of an example.