This paper presents the development and testing of a novel fault tolerant adaptive bioinspired control system applied to a supersonic fighter aircraft. The control configuration uses an immunity-based feedback mechanism to augment a baseline controller consisting of model reference and dynamic inversion approach. The capabilities of the proposed system, addressing different upset conditions, are compared with different control configurations and tested on a motion based simulation environment with a pilot in the loop. One configuration includes the baseline controller augmented with artificial neural networks, whereas an alternative configuration uses the baseline controller augmented with both, neural networks and artificial immune system itself. A set of novel performance metrics were defined to quantify the input activity from the pilot and from the control surfaces in order to investigate the effectiveness and handling characteristics of the different configurations under failure conditions. Optimization of the immune controller parameters was performed for the most relevant failures of the system using a genetic algorithm approach. The results show that the inclusion of the immunity-based mechanism within the control laws scheme improve the tracking performance in terms of pilot input and control surfaces activity under a variety of abnormal flight conditions.