Aircraft System Identification Approach for Control Surface Fault Diagnosis

Abstract

Modern fault detection and diagnosis (FDD) methods are critical to maintaining flight vehicle safety. This thesis presents a model-based FDD approach for identifying control-surface loss of effectiveness on a small, fixed-wing research aircraft. The work considers several real-time system identification methods to estimate changes in control effectiveness and provide fault information to a fault-tolerant control allocation framework. A baseline aero-propulsive model for an experimental aircraft was developed from flight-test data to establish nominal control-effectiveness parameters used to compare fault diagnosis methods. Five real-time estimation methods were formulated and evaluated: exponentially weighted recursive least squares in the time- and frequency-domain, two Lyapunov-based adaptive parameter estimation methods with exponential or finite-time convergence guarantees under a persistence of excitation condition, and an augmented-state extended Kalman filter. These methods were applied to flight data containing an artificially injected stuck left-aileron fault, implemented through a custom maneuver injection capability, with multisine excitation inputs applied to the control effectors. The estimated control-effectiveness parameters associated with the faulted surface displayed an immediate response to the failure and a clear trend towards zero, while the parameters corresponding to healthy effectors remained near nominal values. The resulting estimates were used to construct a time-varying health matrix that scales the nominal control-effectiveness matrix, producing a fault-weighted representation suitable for control allocation and supporting the objective of fault hiding. Overall, this work advances in-flight fault diagnosis by providing real-time parameter estimation for fault-tolerant control allocation, enabling redistribution of control authority to support flight operations.