This thesis describes the design, development, and flight-simulation testing of an integrated Artificial Immune System (AIS) for detection, identification, and evaluation of a wide variety of sensor, actuator, propulsion, and structural failures/damages including the prediction of the achievable states and other limitations on performance and handling qualities. The AIS scheme achieves high detection rate and low number of false alarms for all the failure categories considered. Data collected using a motion-based flight simulator are used to define the self for an extended sub-region of the flight envelope. The NASA IFCS F-15 research aircraft model is used and represents a supersonic fighter which include model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The flight simulation tests are designed to analyze and demonstrate the performance of the immunity-based aircraft failure detection, identification and evaluation (FDIE) scheme. A general robustness analysis is also presented by determining the achievable limits for a desired performance in the presence of atmospheric perturbations.

For the purpose of this work, the integrated AIS scheme is implemented based on three main components. The first component performs the detection when one of the considered failures is present in the system. The second component consists in the identification of the failure category and the classification according to the failed element. During the third phase a general evaluation of the failure is performed with the estimation of the magnitude/severity of the failure and the prediction of its effect on reducing the flight envelope of the aircraft system.

Solutions and alternatives to specific design issues of the AIS scheme, such as data clustering and empty space optimization, data fusion and duplication removal, definition of features, dimensionality reduction, and selection of cluster/detector shape are also analyzed in this thesis. They showed to have an important effect on detection performance and are a critical aspect when designing the configuration of the AIS.

The results presented in this thesis show that the AIS paradigm addresses directly the complexity and multi-dimensionality associated with a damaged aircraft dynamic response and provides the tools necessary for a comprehensive/integrated solution to the FDIE problem. Excellent detection, identification, and evaluation performance has been recorded for all types of failures considered. The implementation of the proposed AIS-based scheme can potentially have a significant impact on the safety of aircraft operation. The output information obtained from the scheme will be useful to increase pilot situational awareness and determine automated compensation.