Immunity-based flight envelope prediction at post-failure conditions

This paper presents the development of a biologically-inspired methodology for flight envelope prediction at post failure conditions. The flight envelope is understood in its most general meaning as the hyper-space of all achievable or desirable relevant variables. The new ranges of these variables at post-failure conditions are the outcomes of the prediction process. Specific algorithms are proposed depending on the affected sub-system and the nature and characteristics of the failure. Actuator, sensor, propulsion system, and structural failures are considered. The proposed methodology is integrated with immunity-based failure detection and identification and benefits from the capabilities of the artificial immune system to address directly the complexity and multi-dimensionality of aircraft dynamic response in the context of abnormal conditions. A hierarchical multi-self strategy is used, in which low-dimensional projections replace the hyperspace of the self thus avoiding numerical and conceptual issues related to the high-dimensionality of the problem. The methodology is illustrated through numerical examples of envelope prediction under elevator locked failure, yaw rate sensor bias, locked throttle, and partially missing horizontal tail