Two Papers presented at the AIAA SciTech 2016 Conference in invited session on Loss of Control
Barron Associates recently presented to papers at the AIAA SciTech 2016 Conference at an invited session on Loss of Control. The first paper, entitled “Development and Pilot-In-The-Loop Evaluation of Robust Upset-Recovery Guidance,” focused on the idea that Aircraft Loss-Of-Control (LOC) has been a longstanding contributor to fatal aviation accidents. The research presented herein is structured to directly address several known contributing and causal factors associated with vehicle upset and LOC. This paper discusses the development and evaluation of an approach to improve flight safety by visually providing closed-loop guidance for upset recovery that is robust to pilot behavior variation and is able to accommodate vehicle failures and impairment. The Damage Adaptive Guidance for piloted Upset Recovery (DAGUR) system provides continuous closed-loop recovery guidance via visual cues to reduce instances of inappropriate pilot reaction and pilot inaction. Adaptation enables the recovery module to provide appropriate guidance even in cases of vehicle damage or impairment. The recovery guidance system is also specifically designed to be robust to variations in pilot dynamic behavior (including behavior associated with high-stress situations). The adaptive recovery guidance is implemented “upstream” of the pilot and provided via visual cues; therefore it does not require modifications to existing flight control software (for fly-by-wire aircraft) and is equally applicable to non-fly-by-wire aircraft. Included desktop simulation and pilot-in-the-loop evaluation results show that the upset recovery guidance system is able to provide effective guidance for recovery from a variety of post-stall and unusual attitude upsets including cases of hardover control surface failures and that the recovery guidance is robust to large variations in pilot dynamic behavior. Additionally, pilots who evaluated the system indicated that they found the guidance to be useful and intuitive, and that it provided timely and measured recovery guidance. Quantitatively, the pilot-in-the-loop evaluation revealed that the recovery guidance significantly reduced subject pilot inceptor frequency content magnitude (energy) and the associated vehicle response.
The second paper, entitled “Virtual Redundancy for Safety Assurance in the Presence of Sensor Failures,” focused on the idea that both autopilot systems and human pilots, particularly human pilots operating in instrument meteorological conditions, rely heavily on sensor feedback to safely control aircraft. The loss of reliable information for even a single state feedback signal can initiate a chain of events that leads to an accident. On small aircraft, hardware redundancy is often impractical and the failure of a single physical sensor could be the triggering event that leads to an accident. On commercial transport aircraft, hardware redundancy is typical for many key sensors, but common-mode failures are a significant hazard that can make hardware redundancy ineffective for achieving the desired system reliability. Barron Associates has recently developed a virtual sensor redundancy approach to enhance flight safety in the event of sensor failures. The approach continuously monitors sensor data and pilot inputs, and uses these in combination with a model of the air vehicle dynamics to identify sensor faults. The effectiveness of the fault detection is ensured through multi-timescale methods that can detect severe faults very rapidly, while also looking over longer time horizons to capture more subtle faults, and by customized statistical tests that provide increased sensitivity for known failure modes. By using knowledge of sensor faults from the fault detection and isolation component, the system generates replacement virtual sensor outputs that are not influenced by the faulty sensor data. The approach also generates estimates of the uncertainty associated with the virtual sensor outputs that can be used in downstream algorithms to mitigate safety hazards.