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Frameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control Systems
- Project Overview -
Janos SztipanovitsISIS-Vanderbilt University
MURI Year 3 Review MeetingFrameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control SystemsUC Berkeley, Berkeley, CADecember 2, 2009
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Team
Vanderbilt Sztipanovits (PI), Karsai, Kottenstette, Neema
Porter, Hemingway, Nile UC Berkeley
Tomlin (PI), Lee, Sastry, Ding, Gillula, Gonzales, Huang, Leung, Lickly, Mahdl, Latronico, Shelton, Tripakis, Vitus
CMU Krogh (PI), Clarke, Platzer
Jain, Lerda, Bhave, Maka Stanford
Boyd (PI)Wang
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• Development of a theory of deep composition of hybrid control systems with attributes of computational and communication platforms • Development of foundations for model-based software design for high-confidence, networked embedded systems applications. • Composable tool architecture that enables tool reusability in domain-specific tool chains• Experimental research
Long-Term PAYOFF: Decrease the V&V cost of distributed embedded control systems
Objectives
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Agenda9:00 – 9:05 am Introductions9:05 - 9:15 am Project Overview
Janos Sztipanovits9:15 – 10:00 am Overview of Hybrid Control Design Challenges and Solutions
Claire Tomlin and Shankar Sastry10:00 – 10:45am Model-Integrated Tool Chain for High Confidence Design
Gabor Karsai, Joe Porter, Graham Hemingway and Janos Sztipanovits 10:45 - 11:00am Break 11:00 – 11:45 am Correctly Composing Components: Ontologies and Modal Behaviors
Edward Lee11:45 – 12:45pm Model-based Testing and Verification
Edmund Clarke, Bruce Krogh, Andre Platzer 12:45 – 1:45pm Lunch
1:45 – 2:15 pm Performance Bounds and Suboptimal Policies for Linear Stochastic ControlYang Wang and Stephen Boyd
2:15 – 2:45 pm Constructive Non-linear Control Design With Applications to Quad-Rotor and Fixed-Wing AircraftNicholas Kottenstette
2:45 – 3:30 pm Starmac Experimental Platform DemoClaire Tomlin and Shankar Sastry
3:30 – 3:45 pm Plans for Year 4&5Janos Sztipanovits 3:45 - 4:00 pm Break 4:00 – 4:30 pm Government Caucus4:30 – 4:45 pm Feedback to the Research Team
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Overall Undertaking
Scope of the Project: Development of component technologies in selected areas Development of model-based design methods Incrementally building and refining a tool chain for an experimental domain
(micro UAV control) Demonstration of control software development with the tool chain Experiments
Model-Based Design
Plant Models and
Requirements
Controller Modeling
System-LevelModeling
SW Architecture
Modeling
DeploymentModeling
Code
X ExpensiveIntractableFragile
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Composition Inside Abstraction Layers
Plant DynamicsModels
Controller Models
Dynamics: • Properties: stability, safety, performance• Abstractions: continuous time, functions,
signals, flows,…Physical design
1( ) ( ( ),..., ( ))p jB t B t B t
SoftwareArchitecture
Models
Software Component
CodeSoftware design
Software : • Properties: deadlock, invariants, security,…• Abstractions: logical-time, concurrency, atomicity, ideal communication,..
1( ) ( ( ),..., ( ))c kB i B i B i
System Architecture
Models
ResourceManagement
ModelsSystem/Platform Design
Systems : • Properties: timing, power, security, fault tolerance• Abstractions: discrete-time, delays, resources, scheduling,
1( ) ( ( ),..., ( ))j p i k iB t B t B t
Assumption: Effects of digital implementation can be neglected
Assumption: Effects of platform properties can be neglected
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Composition Inside Abstraction Layers
Plant DynamicsModels
Controller Models
Physical design
SoftwareArchitecture
Models
Software Component
CodeSoftware design
System Architecture
Models
ResourceManagement
ModelsSystem/Platform Design
Controller dynamics is developedwithout considering implementation uncertainties (e.g. word length, clock accuracy ) optimizing performance.
Software architecture models are developed without explicitly consideringsystems platform characteristics, eventhough key behavioral properties depend on it.
Platform architectrue defines platform configuration, resource management, networking,. Uncertainties introduce time variant delays that may require re-verification of key properties on all levels.
Assumption: Effects of digital implementation can be neglected
Assumption: Effects of platform properties can be neglected
X
X
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Model-Based Design
Plant Models and
Requirements
Funcion (Controller) Modeling
System-LevelModeling
SW Architecture
Modeling
DeploymentModeling
Code
Improve Robustness of Controllers Against Implementation Uncertainties
How should we increase robustness in controller design?– Robust hybrid and embedded systems design (Tomlin, Sastry)– Performance bounds for constrained linear stochastic control
(Boyd, Wang)– Constructive nonlinear control design (Kottenstette, Porter)
Controller Design
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Model-Based Design
Plant Models and
Requirements
Funcion (Controller) Modeling
System-LevelModeling
SW Architecture
Modeling
DeploymentModeling
Code
Verification and Testing
How can we exploit heterogeneous abstractions in verification and test generation? – Model-based testing and verification of embedded systems
implementations (Clarke, Platzer)– Statistical Probabilistic Model Checking (Zuliani, Clarke)
V&V
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Model-Based Design
Plant Models and
Requirements
Funcion (Controller) Modeling
System-LevelModeling
SW Architecture
Modeling
DeploymentModeling
Code
Model-based code generation (2008)
From ModelsTo Code
How to design high-confidence software and systems?– Model-based code generation with partial evaluation (Zhou,
Leung, Lee)– Model-based code generation with graph transformation
(Karsai)
(Last year results, they are built in the tools.)
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Model-Based Design
Plant Models and
Requirements
Funcion (Controller) Modeling
System-LevelModeling
SW Architecture
Modeling
DeploymentModeling
Code
Progress towards integrated model-based design flow
How can we integrate model-based design flows? – Correctly composing components (Lee)– Model-integrated tool chain for high confidence design
(Karsai, Porter, Hemingway, DeBusk and Sztipanovits)– StarMac Experimental platform (Tomlin, Sastry)
PRISMMeta-Model
ECSL-DP Meta-Model
AIRESMeta-Model
CFGMeta-Model
PRISMESML
ESML- CFG
ESML AIFModel-Based Design
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Starmac Experimental PlatformQuadrotor aircraft developed by co-PI Claire Tomlin
Requires integration of legacy and custom components.
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Experimental Set Up
A mobile sensor network:– A set of vehicles, each with a set of sensors for its own
navigation and control, as well as for sensing its environment (such as target range or bearing)
– Computation is distributed, and limited to the processors on board the vehicles (no central computer)
– Communication between subsets of vehicles (limited by range or geography) available
– Collision avoidance needed between vehicles– Humans share control with automation
Focus on algorithms for autonomous search:– Unexploded ordinance detection– Beacon tracking scenarios– RFID tracking– Survey of disaster areas– Search and rescue– Biological studies, animal monitoring
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Accomplishment Highlights 1/2
New results in hybrid control system design using reachable set analysis. Methodology for computing reachable sets using quantized inputs over discrete time steps has been developed and implemented for an aircraft collision avoidance example. (Tomlin, Sastry)
Use of reachable set analysis in complex control law design. (Tomlin) We have extended our approach for integrated software model checking in
the loop to the case of nonlinear dynamic plant models using the concept of bisimulation functions for nonlinear systems (Krogh) (not presented at the review)
New algorithm for the formal verification of curved flight collision avoidance (Clarke, Platzer)
New algorithm and method for statistical probabilistic model checking and its application to Simulink/Stateflow models (Clarke, Zuliani)
Extension of passivity based approach for controller design to fixed-wing aircrafts. (Kottenstette)
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Accomplishment Highlights 2/2
New results in introducing ontology information using Hindley-Milner type theories in modeling environments (Lee)
New results in handling time in hierarchical modal models (Lee) Integrated tool chain for model-based generation of embedded flight
controller on distributed computing platform. Guaranteed stability against implementation induced timing uncertainties and verified schedulability on time-triggered platform.
Demonstration of roundtrip engineering between physical and implementation layers: physical models are used for code generation and implementation models are used for updating physical models.
Demonstration of practical use of reachable set analysis in acrobatic maneuver design and multi-vehicle collision avoidance for the STARMAC quadrotor helicopter testbed.
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Collaboration
The team members work together extensively in many areas in this project and outside of the project
Many examples for joint work among research teams
Forms of collaborations:– Bi-weekly/monthly telecons– Researcher and graduate student visits– Free flow of ideas, methods and tools
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Transitioning The Ptolemy II source tree now is available via CVS. The team
actively works on transitioning research results to the following companies :
Lockheed Martin National Instrument
Vanderbilt’s MIC tool suite (GME, GReAT, UDM, OTIF) had a major release in 2009. GME supports now large scale model management and concurrent modeling. The releases are available through the ISIS download site.
Vanderbilt continued working with GM, Raytheon, LM and BAE Systems research groups on transitioning model-based design technologies into programs.
Vanderbilt continued working with Boeing’s FCS program on applying the MIC tools for precise architecture modeling and systems integration.
Active collaboration with TTTech, University of Vienna. Collaboration started with VERIMAG.on integrating BIP in the tool chain.
UC Berkeley’s reachable set tools are transitioned to the following institutions:
Microsoft Research NASA Ames
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Plans for Years 4&5 Networked Control System Design
– Distributed control/multi agent systems– Dynamic state estimation and mode switching– Robustness against network effects– More realistic channel models– Managing effects from network layer
Verification and Testing– Generation of formal representations from models– Order reduction using hybrid bisimulation– Compositional specification of heterogeneous components
Tools– Integrated, heterogeneous tool chains – Complete path from virtual prototyping to physical implementation– Additional design aspects: fault management, bridge to security
Experiments – Extension of scope and complexity
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FUNDING ($K)—Show all funding contributing to this projectFY06 FY07 FY08 FY09 FY10 FY11
AFOSR Funds 479 986 989 547Option 465 995 529
TRANSITIONS• Strong link to industry: Boeing, BAE Systems, Raytheon, GM,
MathWorks, National Instruments, TTTech• Industry affiliate programs: CHESS, ESCHER, GMLab.
STUDENTS, POST-DOCS• 9 graduate students (MURI) + student groups from other
projects
LABORATORY POINT OF CONTACT Dr William M. McEneaney, AFRL/AFOSR Dr Fariba Fahroo, AFRL/AFOSR Dr. David B. Homan , Civ AFRL/RBCC, WPAFB, OH
APPROACH/TECHNICAL CHALLENGES
• Guaranteed behavior of distributed control software using the following approaches: (1) extension of robust controller design to selected implementation error categories (2) providing “certificate of correctness” for the controller implementation (3) development of semantic foundation for tool chain composition (4) introducing safe computation models that provide behavior guarantees
ACCOMPLISHMENTS/RESULTS See Presentations
Long-Term PAYOFF: Decrease the V&V cost of distributed embedded control systems OBJECTIVES• Development of a theory of deep composition of hybrid control systems with attributes of computational and communication platforms • Development of foundations for model-based software design for high-confidence, networked embedded systems applications. • Composable tool architecture that enables tol reusability in domain-specific tool chains• Experimental research
Frameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control Systems
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ModelTransformation
Modeling LanguagesModels
Model TranslatorsModel-based Code Generators
Analysis toolsPlatforms
Control DesignImplementation Design
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WiFi802.11b
≤ 5 Mbps
ESC & MotorsPhoenix-25, Axi 2208/26
IMU3DMG-X1
76 or 100 Hz
RangerSRF08
13 Hz Altitude
GPSSuperstar II
10 Hz
I2C400 kbps
PPM100 Hz
UART19.2 kbps
RobostixAtmega128
Low level control
UART115 kbps
CF100 Mbps
Stereo CamVidere STOC
30 fps 320x240
Firewire480 Mbps
UART115 Kbps
LIDARURG-04LX
10 Hz ranges
RangerMini-AE
10-50 Hz Altitude
BeaconTracker/DTS
1 Hz
WiFi802.11g+
≤ 54 Mbps
USB 2480 Mbps
RS232115 kbps
Timing/Analog
Analog
RS232
UART
Stargate 1.0Intel PXA255
64MB RAM, 400MHzSupervisor, GPS
PC/104Pentium M
1GB RAM, 1.8GHzEst. & control
Start with controller
Expand to supervisor
Finally to host
Starmac Platform
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Platform Extensions
TTTech
MPC 555 micros TTP/C comm TTTech Software tools Fault-tolerance
Soekris
Linux w/ 3xEthernet TT Virtual Machine on
standard UDP and Linux No fault tolerance (yet)
Gumstix