This paper presents a novel bio-inspired adaptive control technique that has been designed to maintain the performance of an aircraft under upset conditions. The proposed control approach is inspired by biological principles that govern the humoral response of the immune system of living organisms and is intended to reduce pilot effort while maintaining adequate aircraft operation outside bounds of nominal design. The immunity-based control parameters are optimized offline for multiple sets of failures using a genetic algorithm approach. The performance of the immunity-based augmentation is compared with a neural network (NN)-based augmentation. Different piloted tests were performed on a six degrees-of-freedom (6DOF) motion-based simulator for different types of maneuvers under several flight conditions. The results show that the artificial immune system (AIS) proposed scheme improves the aircraft handling qualities by reducing the tracking errors (TEs) and improving the pilot response required to maintain control of the aircraft under upset conditions.

In this paper, two approaches are proposed and compared for the detection and identification of aircraft subsystem failures based on the artificial immune system paradigm combined with the hierarchical multiself strategy. The first approach relies on the heuristic ranking of lower order self/non-self projections and the generation of selective immunity identifiers through structuring of the non-self. The second approach is based on an information processing algorithm inspired by the functionality of the dendritic cells. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets information from the multiple selves to produce a unique detection and identification outcome. A hierarchical multi-self strategy is used with both approaches considering 2-dimensional self/non-self projections or subselves. A mathematical formulation of the concepts and detailed implementation algorithms are presented. The proposed methodologies are demonstrated and compared using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, both detection and identification schemes achieve excellent detection and identification rates with practically no false alarms.

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This paper presents the development and testing of a novel fault tolerant adaptive control system based on a bio-inspired immunity-based mechanism applied to an aircraft fighter model. The proposed baseline control laws use a non-linear dynamic inversion and model reference adaptive control on the inner loops of the aircraft dynamics. In this new approach, the baseline controllers are augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response. The effectiveness of the approach is demonstrated through a full 6 degrees-of-freedom aircraft model interfaced with a Flight gear environment. The performance of the proposed control laws are investigated under a novel set of performance metrics, which quantify the level of input activity from the pilot and from the control surfaces in order to ensure the stability and performance of the aircraft under different actuator and structural failures. Optimization of the parameters of the artificial immunity system is performed using a genetic algorithm. The results show that the optimized fault tolerant adaptive control laws improve significantly the failure rejection using minimum pilot input and control surfaces activity under upset flight 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 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.