1. Research Heaven, West Virginia Verification and Validation of Adaptive Systems Bojan Cukic, Eddie Fuller, Marcello Napolitano, Harshinder Singh, Tim Menzies, Srikanth Gururajan, Yan Liu, Sampath Yeramalla West Virginia University

2. Research Heaven, West Virginia 2 Overview Introduction and motivation. Novelty detection: the data sniffing approach. Stability monitoring Experimentation Current status and perspectives.

3. Research Heaven, West Virginia 3 Introduction System performance evolves over time. –Evolution needed to address complex interactions between system and the environment. –Improved performance achieved through learning. Off-line and on-line adaptation. –Proper reactions to conditions not envisioned/identified/analyzed by system designers. NASA interests. –Autonomous navigation. –Intelligent flight control.

4. Research Heaven, West Virginia 4 Architectures for Adaptive NN-based Control Physical Process Feedback Control Adaptive NN Reference Model Command Error Learning Rule DCS Sigma-Pi Multilayer Perceptron (SHL) Radial Basis Function Desired Response Actual Response + -

5. Research Heaven, West Virginia 5 NASA IFCS Architecture (Gen 1) New V&V Techniques

6. Research Heaven, West Virginia 6 NASA IFCS Architecture (Gen II) pilot inputs Sensors Model Following Control Allocation Dynamic Inversion Controller - Direct Adaptive Neural Network + Feedback Error Implemented In ARTS II Implemented In SCE 3 New V&V Techniques

7. Research Heaven, West Virginia 7 Our Approach to V&V Feedback Control Adaptive NN Learning Rule Desired Response Physical Process Reference Model Command Error Actual Response + - Monitor Inputs to NN, Novelty Detection Monitor Stability of Learning Estimate Trustworthiness of Outputs 1. 2. 3.

8. Research Heaven, West Virginia 8 The Role of Novelty Detection Block the penetration of unreliable or unreasonable training data into the online adaptive component. “Warn” the system about the incoming uncertainty. Provide architectural framework for backward and forward recovery capabilities.

9. Research Heaven, West Virginia 9 Support Vector Machines (SVM) Developed by Vapnik et. al., SVM is designed based on the Structural Risk Minimization Theory. In pattern recognition, used for efficient clustering in highly dimensional spaces. What SVM does? –Maps input space (by means of a nonlinear transformation) into a high dimensional (hidden) feature space.

10. Research Heaven, West Virginia 10 SVM finds the maximum margin hyperplane in the feature space. This hyperplane maximizes the minimum distance to the closest training point. Support Vector Machine (2) The maximum margin hyperplane is represented as linear combination of training points with non-zero weights (called support vectors).

11. Research Heaven, West Virginia 11 Support Vector Data Description Developed by Tax et. al., Finds a sphere with the minimal volume that contains all data points. Basically a one class classifier.

12. Research Heaven, West Virginia 12 Support Vector Data Description (2)

13. Research Heaven, West Virginia 13 5 Failure Modes, 3 Pairs of Parameters. Mode 1: Actuator Failure – stuck left stabilator at current position. ( 0 degree ) Mode 2: Actuator Failure – stuck left stabilator at pre-defined deflection. (+3 degree) Mode 3: Actuator Failure – stuck left stabilator at pre-defined deflection. (-3 degree) Mode 4: Actuator Failure – stuck right aileron at pre-defined deflection. (+3 degree) Mode 5: Actuator Failure – stuck right aileron at pre-defined deflection. (-3 degree) Pair 1: ( the pitch rate, the average of the left stabilator and the right stabilator) Pair 2:( the angle of attack, the average of the left stabilator and the right stabilator) Pair 3: ( the pitch altitude, the average of the left stabilator and the right stabilator) Experiments

14. Research Heaven, West Virginia 14 Data Collection

15. Research Heaven, West Virginia 15 SVDD and IFCS Simulation

16. Research Heaven, West Virginia 16 Novelty Detection for IFCS Parameterization of SVDD affects the treatment of novelties

17. Research Heaven, West Virginia 17 Monitoring Stability of Learning Traditional V&V techniques for a Neural Network code are not applicable. –The system changes following the deployment. The goal of the proposed approach: –Prove stability property of the learner. –Monitor convergence towards the stable state of the learner in real-time. –Provide a time varying measure of reliance that can be justifiably placed on the adaptive component. Smells like dependability, doesn’t it?

18. Research Heaven, West Virginia 18 Lyapunov (like) Stability Analysis We emphasize the role of Lyapunov-type self-stabilization analysis for V&V. –Think of NN as dynamical system –Lyapunov’s theorem says: find V such that V>0 and  V·0 then system moves towards given solution (stable). –Perturbations tend back to given solutions so variations in data will still give learnable states. DCS Neural Network algorithm is internally self-stabilizing.

19. Research Heaven, West Virginia 19 Lyapunov Theory and V&V Built in to this approach is a real-time measure for self- correction of a neural network based adaptive system

20. Research Heaven, West Virginia 20 A Simple Example Learning in DCS neural network. Two Spirals data set. –Move neurons to match data. –Connect regions that represent similar data. –Grow network. –Monitor self-correction.

21. Research Heaven, West Virginia 21 Stabilization Within DCS NN THEOREM: –During DCS network’s learning and representation from a fixed input manifold, the evolving state of the network due to neural unit’s positional adjustment is self-stabilizing in a globally asymptotically stable manner.

22. Research Heaven, West Virginia 22 Experimental Evaluation An F-15 simulator customized for WVU/NASA research needs. Input data sets obtained from simulated flights. DCS Cz network (Mach, altitude, alpha (angle of attack) WVU F-15 Flight Simulator

23. Research Heaven, West Virginia 23 Experimental Evaluation (2) Input data set (blue) Network training (red/green) Lyapunov monitoring function

24. Research Heaven, West Virginia 24 Summary The project is developing fundamentally new V&V technology applicable to a fundamentally different type of systems. Critical for future NASA missions (ASAP). –Autonomy and adaptivity anticipated to be critical. Multidisciplinary approach in research is a key to success. Research solves an engineering problem, which happens to have software implementation. This is a systems engineering exercise.

25. Research Heaven, West Virginia 25 Summary (2) Separating nominal flight conditions from failure modes by SVDD appears very promising research direction. –Computationally efficient. Lyapunov self-stabilization theory can guarantee that the network actually preserves and learns the input feature data manifold –The central V&V issue of IFCS. Effective monitoring of performance aspects of the neural network controllers.

26. Research Heaven, West Virginia 26 Further work Investigating real-time application of SVDD. Extending Lyapunov type analysis to variable input manifolds. –Stochastic stability analysis. Continual maintenance of the flight simulation capability. Investigating the “output trustworthiness” issue. Continuing collaborations with NASA (Ames, Dryden, IV&V), ISR, contractors… Keeping up with V&V needs of GEN II and beyond.