Verification and Validation of Adaptive Systems
description
Transcript of Verification and Validation of Adaptive Systems
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
3
Research Heaven,West VirginiaIntroduction
• 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
Architectures for Adaptive NN-based Control
Physical Process
Feedback ControlAdaptive NN
Reference Model
Command
Error
LearningRule
•DCS•Sigma-Pi•Multilayer Perceptron (SHL)•Radial Basis Function
Desired Response
ActualResponse+
-
5
Research Heaven,West Virginia
R e a l-T im e P ID
e s tim a te dd e r iv a tiv e s
d e r iv a tiv ee rro rs
C o n tro lle rp ilo tin p u ts
S e n s o rs
c o n tro lc o m m a n d s
b a s e lin ed e r iv a tiv e s
B a s e lin eN e u ra l N e tw o rk
O n lin eN e u ra lN e tw o rk
d e r iv a tiv ec o rre c t io n s
a n a lo g
NASA IFCS Architecture(Gen 1) New V&V Techniques
6
Research Heaven,West Virginia
NASA IFCS Architecture (Gen II)
pilotinputs
Sensors
ModelFollowing
ControlAllocation
Dynamic InversionController
-
Direct AdaptiveNeural Network
+Feedback
Error
ImplementedInARTS II
ImplementedIn SCE 3
New V&V Techniques
7
Research Heaven,West Virginia
Our Approach to V&V
Feedback ControlAdaptive NN
LearningRule
Desired Response
Physical Process
Reference Model
Command
Error
ActualResponse
+-
Monitor Inputs to NN,Novelty Detection
Monitor Stabilityof Learning
EstimateTrustworthinessofOutputs
1.2.
3.
8
Research Heaven,West Virginia
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
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
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
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
Support Vector Data Description (2)
13
Research Heaven,West Virginia
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
Data Collection
15
Research Heaven,West Virginia
SVDD and IFCS Simulation
16
Research Heaven,West Virginia
Novelty Detection for IFCS
Parameterization of SVDD affects the treatment of novelties
17
Research Heaven,West Virginia
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
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
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
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
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
Experimental Evaluation
• An F-15 simulator customized for WVU/NASA research needs.
• Input data sets obtained from simulated flights.
• DCSCz network (Mach, altitude, alpha (angle of attack)
WVU F-15 Flight Simulator
23
Research Heaven,West Virginia
Experimental Evaluation (2)
• Input data set (blue)
• Network training (red/green)
• Lyapunov monitoring function
24
Research Heaven,West Virginia
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
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
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.