Immunity-based flight envelope prediction at post-failure conditions

Abstract

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 subsystem 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 multidimensionality 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.

Risk Quantification and Visualization Method for Loss-of-Control Scenarios in Flight

“…In order to construct a risk assessment matrix containing all risk-related flight parameters, five basic colors representing risk levels should be specified numerical values (i.e., risk spectrum values). Consequently, the risk spectrum value of the i-th risk-related flight parameter c i (t) is determined by Equation (13). It should be noted that the difference of risk spectrum value of neighboring colored intervals gradually increased from the center to both sides for distinguishing incident conditions from safer flight conditions.…”

“…The flight envelope estimation and protection system is an augmentation to a conventional flight management system that prevents loss-of-control events [10,11]. Several methods, such as neural networks [12] and immunity-based methods [13], have been used to reliably identify the limiting flight condition boundaries, such as in the case of elevator or throttle failure. Additionally, control schemes such as fault-tolerant control [14], adaptive sliding mode control [15], and linear adaptive control [16] have been proposed to prevent aircraft with failures from exceeding the flight envelope.…”