Forutsigbar effekt i systemer med
funksjonalitet for å tilpasse seg
uforutsigbare endringer
SINTEF IKT Seminar
2010-09-30
André A. Hauge
Institutt for energiteknikk - IFE
Sector ● MTO
1
22.10.2010
Change is good
SINTEF IKT Seminar
2010-09-30
André A. Hauge
Institutt for energiteknikk - IFE
Sector ● MTO
2
22.10.2010
Self-adaptive software
what is it
what is it good for
case
Sector ● MTO
3
22.10.2010
Self-adaptive software...
”...evaluates its own behaviour and changes behaviour
when the evaluation indicates that it is not
accomplishing what the software is intended to do, or
when better functionality or performance is possible”
[Laddaga 1997]
”...modifies its own behaviour in response to changes in
its operating environment...”
[Oreizy et al. 1999]
Sector ● MTO
4
22.10.2010
Self-* properties
Self-configuring
Self-healing (Self-repairing, Self-diagnosing)
Self-optimizing (Self-tuning, Self-adjusting)
Self-protecting
Self-awareness (linked to Self-monitoring)
Context-awareness
[Salehie & Tahvildari]
Sector ● MTO
5
22.10.2010
Good for...
Adaptive systems proposed
and explored
in many different domains, e.g.;
Health [Beda et al. 2010]
Nuclear Power Production [Nestorov et al. 09]
Space exploration [Sierhuis et al. 03]
Millitary applications [Soares et al. 06, Tallant et al. 06]
Motivation is to increase performance or
Safety by applying techniques for handling
variations in dynamics, unknown variations,
unforseen events
Sector ● MTO
6
22.10.2010
NASA: ”This is a survivable accident”
Sector ● MTO
7
22.10.2010
NASA IFCS (Intelligent Flight Control System)
The IFCS project aim was to provide increased
resiliency to extreme changes in
air plane flight behaviour. Destabilizing failures was
simulated in order to challenge an adaptive system to
compensate for failures during
operation and stabilize the plane
Sector ● MTO
8
22.10.2010
Sector ● MTO
9
22.10.2010
Neural network-based flight control technology
Direct adaptive approach, neural network output are
applied directly to the flight control system feedback errors
Preliminary flight tests of an pre-trained neural network to
NF-15B's aerodynamic database were flown in Spring 1999
IFCS Generation I flight tests, flown in 2003, onboard
algorithms to identify changes in aerodynamic characteristics. It
used a Neural Network to organize and map these aerodynamic
changes and provide information to control system
Sector ● MTO
10
22.10.2010
Risk reduction flights were flown in 2005 in preparation
for the Generation II tests
Generation II flight tests allow the neural networks to
take more direct control. flown in period 2006-2008
Generation II neural network takes 31 inputs from the
roll, pitch, and yaw axes and the control surfaces to measure
the aircraft state
Sector ● MTO
11
22.10.2010
Technical Solution – IFCS Gen II
Sector ● MTO
12
22.10.2010
NASA Conclusion on IFCS project
Able to compensate for adverse flight conditions
with adaptive neural network
Flight test showed promising result
Validation methods not likely strong enough for
production program
Sector ● MTO
13
22.10.2010
Self-info
André A. Hauge
PhD student at UiO
Project title: Safe Adaptive Control
Started: 01.09.2008
Ends: 31.08.2012
Project funded by: Institutt for energiteknikk
Sector ● MTO
14
22.10.2010
Thank you for your time!
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22.10.2010
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16
22.10.2010