FLEXCON

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FLEXCON

• Flexible Embedded Control Systems

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FLEXCON Real-Time Control
Real-Time Computing
Control Engineering
Control in Real-Time Computing
Real-Time Techniques in Control System
Implementation
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Real-Time Embedded Systems
Component Technology

• Feedback Control
• Application area
• Technology for handling uncertainty and
  provide flexibility

Flexibility
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Background

• SAVE
• Research programme
• 17 MSEK /3 years

• ARTES
• Real-time systems research network and
  graduate school
• 88MSEK / 5 years

• FLEXCON
• Research programme
• 10MSEK / 3 years
• 2003-2005

• ARTES
• Graduate school
• 7 MSEK/ 2 years
• 2004-2005

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Partners

• Academic
• LTH/Aut.Control (Karl-Erik Årzén - progr.
  director)
• LTH/Computer Science (Klas Nilsson)
• KTH/DAMEK (Jan Wikander)
• University of Skövde (Sten F. Andler)
• Mälardalen University (Ivica Crnkovic, Gerhard
  Fohler)
• Industrial
• ABB Robotics
• ABB Automation Technology Products
• Enea
• (Other industry partners, e.g.,within LUCAS)

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Budget

• 10 MSEK over three years
• 5 PhD positions
• One 33 post-doc position
• Demonstrator
• Administration

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Focus

• Provide flexibility and dependability in embedded
  control systems implemented with COTS
  component-based computing and communications
  technology
• Use control-theoretical approaches as a way of
  handling uncertainty and provide flexibility
• Quality-of-Service approaches in control systems
• Testing-based verification of control systems

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Keywords
Event-driven
Flexibility
Adaptability
Feedback control
Uncertainty
Openness
Run-time approaches
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Temporal Determinism

• The key issue in hard real-time systems
• However, temporal determinism is not a yes or no
  thing.
• Levels of determinism.
• Designing a system to be temporally deterministic
  is not always cost-effective
• Control technology developed to master
  uncertainty
• Use it in real-time systems!

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Temporal Non-Determinism

• Decreases
• improvements in worst-case analysis methods
• tool development
• development of more deterministic implementation
  techniques
• Increases
• developments in general purpose computer systems
• new types of applications, e.g., Internet-based,
  operating in open and unpredictable environments
• next generation micro-chips
• stochastics will play a larger role
• sacrifice temporal determinism to maintain
  functional determinism

My belief is that the increase will dominate
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Flexibilitet

• F m.a.p osäkerhet om resursutnyttjande
• F m.a.p osäkerhet om egenskaper hos
  implementationsplattform
• F m.a.p. osäkerhet om extern omgivning
• F m.a.p. osäkerhet om tasks (last)
• F m.a.p. specifikationer (interval/max/min vs
  fixa värden)
• F m.a.p. dynamisk systemuppdatering (plugn play)
  (komponenter, applikationer, systemprogramvara)
• F. i bemärkelsen event-triggered vs
  time-triggered (dynamic vs static)
• F. i utvecklingsprocessen (vid design-time),
  använda komponenter etc, konfigurering,
• F. m.a.p. virtuell resp fysisk miljö

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WP1 Flexibility in real-time embedded control
system design using COTS platforms, languages and
components

• Component Technology (Ivica Crnkovic)
• embedded control systems
• real-time issues
• flexibility
• PhD student Johan Fredriksson (2003) (SAVE)
• Language Technology Java (Klas Nilsson)
• dynamic aspects
• flexibility
• PhD student Sven Gestegård Robertz
• Cont. of ARTES project
• Feedback scheduling in dynamic memory allocation
  (RT-Java)

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WP23

• WP2 Control-Based Approaches in Embedded Systems
• WP3 Quality-of-Service and Resource Negotiation
  in Embedded Control
• Combined into a single WP with focus on control
  systems

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Temporal Determinism

• Computer-based control theory is based on
• equidistant sampling
• negligible input-output latencies that can be
  ignored or constant latencies that easily can be
  compensated for
• Reality
• Varying execution times due to preemption,
  blocking, data-dependencies, caches, pipelines,
  network communication,
• Result
• Sampling interval jitter
• Non-negligible and varying latencies

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Control Community
A new implementation and resource-aware control
paradigm is needed!
Resource-Constrained Control
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Hard Control Implementation Approach

• Strive to maximize the temporal determinism
• E.g. using time-triggered and synchronous
  programming models
• Pros
• Simplifies attempts at formal verification for,
  e.g. safety-critical applications
• However, a large amount of hard real-time
  control applications are not safety-critical
• Cons
• Often requires special purpose solutions, i.e.,
  less efficient and more expensive
• Requires complete knowledge about resource
  utilization, load, ..
• May result in under-utilized systems with
  possibly poor control performance

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Hard R-T Task Model

• Periodic/sporadic tasks with constant period,
  hard deadline, and known WCET
• Just a model
• Does not fit all control problems
• E.g. hybrid controllers, event-based controllers
• Overly restrictive for most control problems
• a missed deadline no catastrophy
• a late control signal is better than no signal at
  all

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Soft Control Implementation Approach

• View the temporal nondeterminism caused by the
  implementation platform as an uncertainty or
  disturbance acting on the control loop
• Use control-based approach
• Inherent robustness of feedback
• Design for robustness against implementation
  uncertainties
• Active compensation, cp feedforward from
  measurable disturbances and adaptive control

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Implementation-Robust Control
A tremendous amount of theory for plant
uncertainties
?
?
Very little theory for implementation platform
uncertainties
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Implementation-Robust Control

• Temporal robustness
• timing variations
• Theory that allows us to decide which level of
  temporal determinims that a given control loop
  really requires in order to meet given objectives
  on stability and performance
• Is it necessary to use a time-triggered approach
  or will an event-triggered approach do?
• How large jitter in sampling interval and i-o
  latency can be tolerated?
• Is it Ok to now and then skip a sample?
• ..
• Functional robustness
• Fault-tolerance towards computer-level faults
  leading to data errors
• An increasing problem in future deep sub-micron
  technology hardware

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Resource Allocation as a Control Problem

• In an applications with multiple (control) tasks
  the dynamic allocation of resources to the tasks
  can be viewed as a control problem in itself!
• The control performance can be viewed as a
  quality-of-service attribute (Quality-of-Control)

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Control in Real-Time Computing

• Use of control-based approaches for uncertainty
  management in large real-time computer and
  communication systems is receiving increased
  attention
• The worst-case approach no longer feasible
• Feedback, feedforward, ...
• Control-oriented models capturing dynamics

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Feedback Scheduling

• Dynamic on-line allocation of computing resources
• Feedback from actual resource utilization
• In principle, any computing resource

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Feedback Scheduling Structures

• Feedback
• Reactive
• Feedforward
• Proactive
• Mode changes and admission control

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Requirements on Scheduling Theory

• Relax the standard hard-real time assumptions
• Theory that better matches the needs of control
  systems

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Requirements on Control Theory

• Co-design methods
• control design methods that take resoure
  constraints into account
• Improved understanding of how temporal
  non-determinism effect control performance
• analysis methods
• Tools
• Theory for aperiodic systems

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Examples of recent developments

• Jitterbug (Cervin, Lincoln)
• Matlab toolbox
• analysis of how sampling period and i/o delay
  distributions effect control performance
• TrueTime (Cervin, Henriksson)
• Simulink toolbox
• co-simulation of temporal effects of real-time
  kernels and communication networks, and control
  performance
• New simple stability results (Lincoln)
• control loops with variations in delay
• networked control loops

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Jitterbug
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TrueTime
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Tool Usage
Simulation withTrueTime
Analysis withJitterbug
SchedulingParameters (T,D,Prio, )
Task TimingParameters (latencies, jitter, )
ControlPerformance (variance, rise time,
overshoot, .)
Non-trivialrelationship
Complex, nonlinearrelationship
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People Involved

• PhD student Dan Henriksson LTH/AC
• Feedback scheduling for control systems
• MPC controllers
• Control in real-time computing
• Web-servers
• Univ of Virginia (Stankovic/Abdelzaher)
• PhD student Damir Isovic Mdh (2004)
• Adaptive scheduling
• PhD student Martin Sanfridsson KTH (2003)
• Cont. of ARTES
• Quality of service in control
• Thesis during fall

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WP4 Testing-Based Verification and Monitoring of
Embedded Control Systems

• Högskolan i Skövde (Sten Andler)
• Focus on event-driven control systems
• Run-time properties for testability
• Test case selection and generation.
• PhD students
• Robert Nilsson, 40 (cont. of ARTES)
• Birgitta Lindström, 40 (cont. of ARTES)
• Connection to MdH (Thane)

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WP5 Robotics and Automation Demonstrator

• Common platform for demonstrating project results
• Maintain the project focused
• Not a moon-lander demonstrator
• Based on Robotics Laboratory in Lund (Klas
  Nilsson)
• EU project Hard R-T Corba (HRTC)
• EU project AUTOFETT with ABB,
• Strong links to ABB
• People
• Klas Nilsson Anders Blomdell, LTH

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Joint Activities

• Strong connections with SAVE and ARTES
• groups
• research
• Maintain the connections
• e.g. joint meetings in association with
  ARTES/ARTES Summer schools
• PhD courses

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Related Activities

• ARTIST EU/IST
• FP5 network
• ARTIST2 FP6 Network of Excellence Proposal
• Design of Embedded System
• Seven clusters
• Control in Embedded Systems
• Lund and KTH

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