Transcript 2005 Otis 2D SSA Project

Robust Real-time Control Systems
Reliability through algorithm design, execution and system engineering
Raktim Bhattacharya
Assistant Professor
[email protected]
Department of Aerospace Engineering
H.R. Bright Building, Rm. 701, Ross Street - TAMU 3141
College Station TX 77843-3141
Raktim Bhattacharya
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Paradigm Shift in Design and Implementation of Control Systems
From static offline designs to dynamic online systems that adapt in real time
•
Role of control algorithms is changing
Static Offline
•
Change in implementation
Distributed
Distributed
Multi-Processor
Multi-Processor
Complex
Complex Software
Software
Modularity
Modularity
Data
Data Communication
Communication
Faster
Faster Development
Development Time
Time
Unbounded
Unbounded Time
Time Delays
Delays
High
High Reconfigurability
Reconfigurability
Real-time
Real-time Task
Task Scheduling
Scheduling
Easy
Easy Maintenance
Maintenance
Modification
Modification of
of Control
Control Algorithms
Algorithms
Fault
Fault Tolerant
Tolerant
BENEFITS
COMPLEXITY
Centralized
Centralized
Single
Single Processor
Processor
•
Dynamic Online
What is driving this?
Falling cost of hardware, increasing computational power, increasingly complex control,
algorithms and development of new, low cost micro sensors and actuators.
•
Is there a price?
Yes! Need sophisticated, reliable software to manage distributed collection of components
and tasks.
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Reliability of Real-Time Control Systems
Verification gap expands exponentially with complexity
Consequence of the Expanding Verification Gap
Capability
Less reliable products
Increased failure rate in the field
High cost implications
Resources engaged in fire fighting
Not possible to innovate
No ability for growth
Cannot react to market changes
Competition sensitive
Market penetration is difficult
Verification gap due to rising complexity in embedded systems.
(Source:www.verisity.com)
Complexity in Embedded Systems
• Cell phones : ~ 10 million lines of code.
• Automobiles : ~ 100 million lines of codes.
• Aerospace : ~ 1 billion lines of code.
Verification is Expensive
• 90% time is spent on verification and validation
Cost of Failure
• 100 times more in the field than in the development
stage
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Time
Classification of Uncertainty in Real-time Systems
• System (model error, sensor noise, etc)
• Communication (delays, packet loss, etc)
• Computation ( transient CPU overloads)
• Product Development (software V&V)
Solution?
Guarantee reliability by design, execution and system engineering.
How? Next slide ….
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Uncertainty in System
Design application algorithms robust to system uncertainty
System
Communication
Computation
System Engineering
Uncertainty Description
Model uncertainty, sensor noise, wind gust, etc.
Complexity
Physics.
Mitigation
Design controller K to guarantee robust
performance.
Methods
Robust Control Design techniques, etc.
V&V
Bound on input to output norm, etc.
• This is a well researched area.
• Several techniques exist for robustness
analysis of linear and nonlinear systems.
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Uncertainty in Communication
Design application specific transmission controller and routing algorithm to bound communication
uncertainty
System
Communication
Computation
System Engineering
Uncertainty Description
Delays, packet loss, channel noise, multiple
transmissions, etc.
Complexity
Information
Mitigation
Design controller K to mitigate communication
uncertainty, robust data transmission.
Research at aero.tamu.edu
Methods
Design of Robust Communication Network
Control with communication constraints, packet
based control, filtering, etc.
• Application defines data traffic, data source & topology.
V&V
Bound on delays, data rate, etc.
• Synthesize transmission controller and routing algorithm
based on communication dynamics.
• Guarantee bounds on delay.
• Preliminary research is based on the work by F. Kelly and
G. Vinnicombe, S.Low, J.C. Doyle and F. Paganini.
• Looking at data rate bounds in a dynamic topology as a
switched linear system.
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Design of Robust Communication Network
Model data-rate dynamics using fluid based linear models
System
Communication
Computation
System Engineering
Application
Approach
Design robust communication network for
mobile agents engaged in surveillance.
1. Use fluid based linear models to describe the dynamics of
data rate for small-scale networks
2. Changing topology results in a switched linear system.
Objective
Stabilize node-to-node data rate in the
presence of dynamic topology.
3. Model traffic load as a stochastic process. (Poisson Process,
Erlang Formula, etc).
Assumptions
4. Analyse dynamics of node-to-node data rate.
• Spatial distribution and connectivity of the mobile agents is
described via a graph.
5. Design feedback congestion control algorithm for robustly stable data
rate.
• The graph is assumed to be dynamic in a sense that it adapts to
the movement of the agents.
6. Work based on research by F. Kelly and G. Vinnicombe, S. Low,
J.C. Doyle and F. Paganini.
• The agents are constrained to satisfy certain simple dynamics, i.e.
they cannot stop on a dime, etc.
• The exact trajectories of the agents are governed by a higher-level
algorithm that the agents are implementing; e.g. dynamic sensing
algorithm, surveillance, etc.
G1(t1)
t1
G1(t2)
t2
G1(t3)
t3
Fig1: Large Scale Network as a Composite of Small Scale Networks
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Fig2: Dynamic Topology – Effective Data Rate is a Hybrid System
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Uncertainty in Computation
Implement algorithms as anytime algorithms
System
Communication
Computation
Uncertainty Description
Transient computational overloads, variation in
execution characteristics of code, uncertainty in
resource availability, etc.
System Engineering
Ideal
Decision
Quality
Anytime
Traditional
Time
Complexity
Time Cost
Anytime + Time Cost
Time
Source: Zilberstein
Mitigation
Scheduling of CPU and other resources to
guarantee execution deadline.
Methods
Dynamics scheduling, imprecise
computation, anytime algorithms, etc.
V&V
Bound on runtime, etc.
Figure : Decision quality with respect to time
for Ideal, Traditional & Anytime Procedures
(Source: Zilberstein )
Research at aero.tamu.edu
Anytime Control Algorithms
• In real-time systems, the utility of the decisions degrade with
the time spent on computation.
• The degradation in utility due to cost of time will render
traditional models of computation useless real-time systems
in uncertain environments.
• Anytime algorithms represent a class of algorithms that can
tradeoff quality of solution for computational time.
• For controllers, performance is compromised for computational
time during transient overloads. Stability is never compromised.
• Developed preliminary results for linear time invariant controllers.
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Anytime Control Algorithms
Model Reduction Approach
System
Communication
Computation
System Engineering
Model Reduction
Consider Linear Controllers
Computational time depends on number of
states rejected.
Original Controller
Balanced Realization
Anytime Implementation
Switch from higher order to lower order
controller during transient CPU overload
Reduced Order Controller
C1:High Order Controller
High CPU
Results
• Algorithm is tested on a linear model for longitudinal motion
of a B737-100 TSRV (Transport System Research Vehicle).
• Controller objective is to track flight path angle and velocity
reference signal.
High CPU
Low CPU
• Able to accommodate drop in CPU resources by 35%.
C2 :Low Order Controller
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C3 :Transition Controller
• The closed-loop system is robustly stable, compromised
tracking performance to save CPU time.
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Uncertainty in System Engineering
Model and Platform Based Design Methodology
System
Communication
Computation
System Engineering
Uncertainty Description
Mismatch between requirements & implementation,
verification gap, sub-component interactions, hardwaresoftware interactions, etc.
Complexity
Software testing.
Mitigation
Regression testing, hardware in the loop testing,
code coverage analysis, etc.
Image Source: PARADES
The Shift from Physical Prototyping to Virtual
Prototyping and Integration
Research at aero.tamu.edu
Methods
Model and platform based design of embedded software.
Robust Embedded Software Development
Process
V&V
• Separation of concern between various stages in the design
process.
Validation of requirements with embedded software, high
percentage of code coverage, etc.
• Use formal models to capture functionality and architecture.
• Conduct early validation at each stage before proceeding.
• Map solutions at one stage to solutions in the following stage
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Model and Platform Based Product Development
Enabler for Engineering Effectiveness and Reliability
System
Key Principles:
Communication
Computation
System Engineering
1.
Separation of concern between various stages in the design process.
2.
Use formal models to capture functionality and architecture.
Key
Articulation
Points
Design
Space
Exploration
Specifications
Mapping
Constraints
Platform
A family of alternate solutions
a) Design Flow
Raktim Bhattacharya
b) Design Flow
with key articulation
points
c) Exploration of
alternate solutions at
key articulation points
d) Mapping of
solutions in upper layer
to solutions in lower layer
during integration
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Model and Platform Based Product Development
Key Benefits
System
Communication
Examples:
Computation
System Engineering
Function
Specifications
MAPPING
Define what needs to be done
Constraints
Architecture
Define how it is done
Separation of Architecture from Functionality
Key Benefits:
Mapping of Functionality to Architecture
Capability
Benefits
Early Validation
Reduced turn backs, higher reliability
Platform Flexibility
Lower cost & obsolescence insensitivity
Reuse
Faster development time
Analysis
Quantification of quality & efficiency
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Early
Response
Capability
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New Paradigm in Embedded System Design Process
MBPD and the Design “V”
System
Computation
Communication
System Engineering
System Validation
(Physical Prototype)
FUNC
ARCH
REQ
Platform
Abstraction
SYS
Component Validation
(desktop)
Modeling
Manual
Test vectors
Models
API Platform
Platform
Abstraction
Integration
with
API
Model
Refinement
Targeted
Models
Auto generated
Test vectors
ANSI C
Language
Platform
Abstraction
Code Generator
(RTW)
ANSI C
Code
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Tools for Software and Hardware Modeling
Software modeling tools are more matured than hardware modeling tools.
System
Computation
Communication
System Engineering
TOOLS
MATLAB, Simulink,
Stateflow
ASCET SD
SCADE
Rhapsody (UML)
FUNCTIONAL DESIGN
More matured
TOOLS
Hardware Platform Abstraction, Selection & Analysis
Research Level
UC Berkeley
Raktim Bhattacharya
Univ. of Michigan, Princeton,
Univ. Minnesota
RT-Builder
Tools for functional design are more matured than
tools for hardware abstraction and analysis.
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Technology Maturity
Who is using it?
System
Automotive
Automotive
Aerospace
Aerospace
Computation
Semiconductor
Semiconductor
System Engineering
Industrial
Equipment
Industrial
Equipment
PARADES
Academia
Academia
Communication
UC Berkeley
Model and Platform Based Design framework has been successfully applied to a
diverse group of industries and has potential to become a standard for embedded
systems development.
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Other Research Activities
Guidance Algorithms for Entry Descent Landing
• Apply receding horizon control methodology to achieve better guidance performance (70% improvement).
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Other Research Activities
Real-time Trajectory Generation Toolbox in MATLAB
Problem Formulation
Trajectory Space Approximation
Trajectory generation problem is cast as an optimal
control problem of the following form:
B-Splines are used to transform infinite dimensional
problem to finite dimensional problem.
Cost:
Dynamics:
Constraint:
Solution Process
Transcribe optimal control problem to nonlinear
programming problem.
Test bed
Blimps from Draganfly, vision based positioning, 3 fan
actuation, RF controlled.
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Questions ?
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