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Closing the Certification Gaps in Adaptive Flight Control Software
Author(s) -
Stephen A. Jacklin
Publication year - 2008
Publication title -
aiaa guidance, navigation, and control conference and exhibit
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2008-6988
Subject(s) - certification , computer science , adaptive control , process (computing) , verification and validation , controller (irrigation) , airworthiness , systems engineering , software , gap analysis (conservation) , control engineering , reliability engineering , engineering , control (management) , artificial intelligence , political science , law , operating system , biodiversity , agronomy , ecology , operations management , biology , programming language
Over the last five decades, extensive research has been performed to design and develop adaptive control systems for aerospace systems and other applications where the capability to change controller behavior at different operating conditions is highly desirable. Although adaptive flight control has been partially implemented through the use of gain-scheduled control, truly adaptive control systems using learning algorithms and on-line system identification methods have not seen commercial deployment. The reason is that the certification process for adaptive flight control software for use in national air space has not yet been decided. The purpose of this paper is to examine the gaps between the state-of-the-art methodologies used to certify conventional (i.e., non-adaptive) flight control system software and what will likely to be needed to satisfy FAA airworthiness requirements. These gaps include the lack of a certification plan or process guide, the need to develop verification and validation tools and methodologies to analyze adaptive controller stability and convergence, as well as the development of metrics to evaluate adaptive controller performance at off-nominal flight conditions. This paper presents the major certification gap areas, a description of the current state of the verification methodologies, and what further research efforts will likely be needed to close the gaps remaining in current certification practices. It is envisioned that closing the gap will require certain advances in simulation methods, comprehensive methods to determine learning algorithm stability and convergence rates, the development of performance metrics for adaptive controllers, the application of formal software assurance methods, the application of on-line software monitoring tools for adaptive controller health assessment, and the development of a certification case for adaptive system safety of flight.

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