Learning Algorithms for Preserving Safe Flight Envelope under Adverse Aircraft Conditions

Government and industry agree on the potential of learning algorithms in providing flight safety in the presence of adverse conditions (resulting from, e.g., degraded modes of operation, loss of control, and imperfect aircraft modeling) and reducing aircraft development costs. A major roadblock to their widespread adoption is the lack of a-priori, user-defined performance guarantees to preserve a given safe flight envelope in general and commercial aviation. Current practice relies heavily on excessive flight testing as a means of performing verification and/or development of the tools to validate existing learning algorithms. Besides the cost, the major drawback of excessive flight testing is that it only provides limited performance guarantees for what was tested; the fixed set of initial conditions, pilot commands, and failure profiles. The drawback of current tools to validate existing learning algorithms is that such tools can only provide guarantees if there exists a-priori and complete structural and behavioral knowledge regarding any and all anomalies that might occur. The proposed research will address this fundamental gap in the utilization of learning algorithms for aerospace applications by (1) establishing a new theoretical framework along with necessary and sufficient conditions for guaranteed flight control safety and resilience in the presence of aircraft adverse conditions. Learning algorithms developed using this framework will keep the aircraft trajectories within this a-priori determined envelope (set) in the presence of anomalies. Analytical expressions for robustness margins of the proposed algorithms will also be developed. (2) Methods will be developed to use these algorithms effectively in the pilot decision support display of NASA Ames that indicates the proximity of the aircraft to safe flight boundaries caused by adverse conditions. As a complementary effort to flight control, a set theoretic learning approach will be utilized in estimation theory to support of pilot decision-making via providing real-time aircraft flight health and prediction. (3) The proposed algorithms will be demonstrated in flight tests using CJ-144 fly-by-wire Bonanza aircraft. This research direction will allow the investigators to revisit the proposed theoretical approaches and relax assumptions, if necessary. The novel feature of this research is that the proposed algorithms will have the capability to preserve a given, user-defined safe flight envelope through formal analytical synthesis at the pre-design stage, instead of excessive flight testing during the post-design stage. The proposed research will (1) impact a broad range of applications utilizing learning algorithms that involve but are not limited to safe and effective aircraft control, crew decision-making in complex situations, and CEV/CLV vehicle control, (2) make the general and commercial aviation community aware of the safety benefits of advanced adaptive flight control and estimation methods, (3) disseminate NASA and DoD research into the aviation community, (4) establish a new partnership between flight controls research at MST and WSU, (5) increase the ability of researchers and industries in the states of MO and KS to successfully compete for research funding, (6) give the MO and KS aviation industry a global leading role in using intelligent aviation technologies on civil aviation aircraft, and (7) contribute to the advanced workforce by educating students in advanced flight controls and pilot warning systems. The research outcomes, which include advancement of the theory on verifiable learning algorithms for flight control and pilot awareness systems, simulations at multiple levels of granularity, data collected from ground and flight testing, publications in highly regarded journals and conferences, online seminars, an invited session, and a workshop, will significantly contribute to the current and future NASA research and technology priorities.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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