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SMART-T Project Overview Kurt D. Guenther AS&M / Dryden Flight Research Center July 19, 2004

SMART-T Project Overview Kurt D. Guenther AS&M / Dryden Flight Research Center July 19, 2004. SMART-T Objectives. SMART-T: Strategic Methodologies for Autonomous and Robust Technology Testing The goal of SMART-T is to address V&V issues of adaptive control systems, including neural networks

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SMART-T Project Overview Kurt D. Guenther AS&M / Dryden Flight Research Center July 19, 2004

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  1. SMART-T Project Overview Kurt D. Guenther AS&M / Dryden Flight Research Center July 19, 2004

  2. SMART-T Objectives • SMART-T: Strategic Methodologies for Autonomous and Robust Technology Testing • The goal of SMART-T is to address V&V issues of adaptive control systems, including neural networks • Develop methodologies to design and validate NN controllers • Develop tools and methodologies to support the eventual certification of adaptive systems • Coordinate the community at-large This technology development effort seeks to build the confidence that is needed to intelligently design, test and safely fly adaptive flight controllers.

  3. Where SMART-T Fits In

  4. Flight Test & Eval F-15 Gen II IFCS, C-17 Gen II IFCS, UAV's Confidence Tool Neural Network Evaluator Research Effort • Tools • Sensitivity Tool • Confidence Tool • Neural Network Evaluator • ANCT Tool • Methods (applied to SW life-cycle: design, development, test) • Generic Guide • F-15 Guide • C-17 Guide Tools Toolset, Methods Flight Test Simulation

  5. Research Effort (cont)

  6. Inverse Plant Model Control Law Neural Network Confidence Tool for IFCS Commanded State Filter Control Augmentation Pilot Inputs Measurements Confidence Tool NN Weights The Confidence Tool, based on a Bayesian approach, provides a Measure of how well the neural network is performing at the moment

  7. Inverse Plant Model Control Law Neural Network Sensitivity Analysis The Sensitivity Analysis provides a Measure of Stability in the sense of Lyaponov 2nd Method for Nonlinear Systems Sensitivity Analysis for IFCS Commanded State Filter Control Augmentation Pilot Inputs Measurements

  8. Sensitivity Tool • NN sensitivity tool provides verification of Lyapunov stability bounds by perturbation of the gains and noise parameters. • All current axis learning parameters are robust to gain and noise in Sigma Pi NN, except yaw axis • Tool implementation completed for: • SHL non-ITAR. • SHL and Sigma Pi with VCAS controller designs.

  9. Automated Neural Controller Test Tool (ANCT) Developed under grant to Case Western Reserve U. • Developed to automate Lyapunov boundary estimation using sens. tool • Rich GUI for the test engineer to vary inputs, automate Monte Carlo sim, set performance criteria, analyze outputs • Interacts with Simulink model, automatically populates GUI with internal variables

  10. ANCT Test Generation

  11. NN Evaluator • Developed by Institute for Scientific Research (ISR), Fairmont, WV • “Fault indicators” derived from inspection of the - NN Weighting adaptation law • discrete poles • weight norms • set-point error norms • Lyapunov stability criteria • Lyapunov rate stability criteria • Implemented in F-15 IFCS ARTS II computer

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