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Surface effectiveness estimation for control reconfiguration of impaired aircraft

The problem of variations in control effectiveness caused by control surface damage was examined in this thesis. Three different filters were implemented to estimate remaining surface effectiveness after surface impairment. The first filter, an Extended Kalman Filter (EKF), was used as a parameter estimator for the surface effectiveness of a single surface. The second estimator, a Multiple Model Estimator (MME), was designed and compared to the Kalman Estimator. The MME was derived from simplifications made to an Adaptive Kalman Filter algorithm. The simplifications were done to facilitate real-time operation of the estimator. The third estimator, also an Extended Kalman Filter, was used to address the multiple damaged surface problem. A natural extension of the single surface EKF increases the capability, so that it can track damage to all of the aircraft surfaces simultaneously. Because the expanded filter monitors all of the surfaces, it can be used as an impairment detector. This is an improvement over the other two estimators which only monitor one surface and require an external impairment detection mechanism. The cost of this extension was found in the time required for the multiple surface EKF to converge. The addition of constraints to the multiple surface EKF was shown to reduce estimation time. Convergence properties and computational costs are presented for each of three estimators. These calculations show that the EKF is a computationally more efficient solution to both the single and multiple damaged surface problem. Thesis Supervisor: Walter Hollister Title: Professor of Aeronautics and Astronautics Thesis Supervisor: Tahm Sadeghi, Manager Title: Advanced Flight Control Engineering General Electric Company ACSD