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

2. 2 SMART-T Briefing to OSMA SAS - 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 ● 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. 3 SMART-T Briefing to OSMA SAS - July 19, 2004 Where SMART-T Fits In

4. 4 SMART-T Briefing to OSMA SAS - July 19, 2004 Research Effort ● Tools ● Sensitivity Tool ● Confidence Tool ● Neural Network Evaluator ● ANCT Tool ● Flight Test & Eval ● F-15 Gen II IFCS, C-17 Gen II IFCS, UAV's ● Confidence Tool ● Neural Network Evaluator Tools Flight Test Simulation Toolset, Methods ● Methods (applied to SW life-cycle: design, development, test) ● Generic Guide ● F-15 Guide ● C-17 Guide

5. 5 SMART-T Briefing to OSMA SAS - July 19, 2004 Research Effort (cont)

6. 6 SMART-T Briefing to OSMA SAS - July 19, 2004 Confidence Tool for IFCS Control Law Inverse Plant Model Pilot Inputs Commanded State Neural Networ k Measurements Filter Confidence Tool The Confidence Tool, based on a Bayesian approach, provides a Measure of how well the neural network is performing at the moment Control Augmentation NN Weights

7. 7 SMART-T Briefing to OSMA SAS - July 19, 2004 Sensitivity Analysis for IFCS Control Law Inverse Plant Model Pilot Inputs Commanded State Neural Networ k Measurements Filter The Sensitivity Analysis provides a Measure of Stability in the sense of Lyaponov 2nd Method for Nonlinear Systems Control Augmentation Sensitivity Analysis

8. 8 SMART-T Briefing to OSMA SAS - July 19, 2004 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. 9 SMART-T Briefing to OSMA SAS - July 19, 2004 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. 10 SMART-T Briefing to OSMA SAS - July 19, 2004 ANCT Test Generation

11. 11 SMART-T Briefing to OSMA SAS - July 19, 2004 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