2005 Otis 2D SSA Project

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Robust Real-time Control Systems Reliability
through algorithm design, execution and system
engineering
Raktim Bhattacharya Assistant Professor raktim_at_aer
o.tamu.edu
Department of Aerospace Engineering H.R. Bright
Building, Rm. 701, Ross Street - TAMU
3141 College Station TX 77843-3141
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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

• Change in implementation

• 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

• 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 VV)

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
Computation
Communication
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. VV 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
Computation
Communication
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.
Methods Control with communication constraints,
packet based control, filtering, etc. VV Bound
on delays, data rate, etc.
Research at aero.tamu.edu

• Design of Robust Communication Network
• Application defines data traffic, data source
  topology.
• 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
Computation
Communication
System Engineering
Application Design robust communication network
for mobile agents engaged in surveillance.
Approach 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. 3. Model
traffic load as a stochastic process. (Poisson
Process, Erlang Formula, etc). 4. Analyse
dynamics of node-to-node data rate. 5. Design
feedback congestion control algorithm for
robustly stable data rate. 6. Work based on
research by F. Kelly and G. Vinnicombe, S. Low,
J.C. Doyle and F. Paganini.
Objective Stabilize node-to-node data rate in the
presence of dynamic topology.

• Assumptions
• Spatial distribution and connectivity of the
  mobile agents is described via a graph.
• The graph is assumed to be dynamic in a sense
  that it adapts to the movement of the agents.
• 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.

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Uncertainty in ComputationImplement algorithms
as anytime algorithms
System
Computation
Communication
System Engineering
Uncertainty Description Transient computational
overloads, variation in execution characteristics
of code, uncertainty in resource availability,
etc. Complexity Time Mitigation Scheduling of
CPU and other resources to guarantee execution
deadline. Methods Dynamics scheduling, imprecise
computation, anytime algorithms, etc. VV
Bound on runtime, etc.
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Anytime Control AlgorithmsModel Reduction
Approach
System
Computation
Communication
System Engineering

• Model Reduction
• Computational time depends on number of states
  rejected.

Consider Linear Controllers
Anytime Implementation Switch from higher order
to lower order controller during transient CPU
overload

• 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.
• Able to accommodate drop in CPU resources by
  35.
• 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
Uncertainty Description Mismatch between
requirements implementation, verification gap,
sub-component interactions, hardware-software
interactions, etc. Complexity Software
testing. Mitigation Regression testing, hardware
in the loop testing,code coverage analysis, etc.
Methods Model and platform based design of
embedded software. VV Validation of
requirements with embedded software, high
percentage of code coverage, etc.
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Model and Platform Based Product
DevelopmentEnabler for Engineering Effectiveness
and Reliability

1. Separation of concern between various stages in
   the design process.
2. Use formal models to capture functionality and
   architecture.

Key Principles
d) Mapping of solutions in upper layer to
solutions in lower layer during integration
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Model and Platform Based Product DevelopmentKey
Benefits
Examples
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
Early Response Capability
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New Paradigm in Embedded System Design Process
MBPD and the Design V
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Tools for Software and Hardware Modeling Software
modeling tools are more matured than hardware
modeling tools.
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Technology Maturity Who is using it?
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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
Trajectory Space Approximation B-Splines are used
to transform infinite dimensional problem to
finite dimensional problem.
Problem Formulation Trajectory generation problem
is cast as an optimal control problem of the
following form
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 ?