motivating applications
DESCRIPTION
Motivating applications. (Source: Boeing X45-A). (Source: Northrop Grumman-X47A). (Source: NASA Ames). Hybrid systems. Continuous systems controlled by a discrete logic: embedded systems (autopilot logic) - PowerPoint PPT PresentationTRANSCRIPT
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Decentralized Optimization, with application to
Multiple Aircraft Coordination
Decision Making Under Uncertainty MURI Review,
July 2002
Gökhan Inalhan, Dusan Stipanovic, Claire Tomlin
Hybrid Systems LaboratoryDepartment of Aeronautics and Astronautics
Stanford University
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Motivating applications
(Source: Boeing X45-A)
(Source: Northrop Grumman-X47A)
(Source: NASA Ames)
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Hybrid systems
• Continuous systems controlled by a discrete logic: embedded systems (autopilot logic)
• Coordinating processes: multi-vehicle systems interfacing continuous control with coordination protocols
• Continuous systems with a phased operation: (biological cell growth and division)
xç= f(x;u;d)
continuous systems(control)
discrete systems(computer science)
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Verification: a mathematical proof that the system satisfies a property
Controller synthesis: the design of control laws to guarantee that the system satisfies the property
• Methods give definitive answers, unlike simulation• Often give surprising answers, trajectories which one
might not think to simulate• Reduces development time, cost of certification
Verification and Controller Synthesis
unsafe
initial
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Verification: a mathematical proof that the system satisfies a property
Controller synthesis: the design of control laws to guarantee that the system satisfies the property
• Methods give definitive answers, unlike simulation• Often give surprising answers, trajectories which one
might not think to simulate• Reduces development time, cost of certification
Verification and Controller Synthesis
unsafe
initial
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Verification: a mathematical proof that the system satisfies a property
Controller synthesis: the design of control laws to guarantee that the system satisfies the property
• Methods give definitive answers, unlike simulation• Often give surprising answers, trajectories which one
might not think to simulate• Reduces development time, cost of certification
Verification and Controller Synthesis
unsafe
initial
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Verification: a mathematical proof that the system satisfies a property
Controller synthesis: the design of control laws to guarantee that the system satisfies the property
Safety Property can be encoded as a condition on the system’s reachable set of states
Verification and Controller Synthesis
unsafeunsafe initialization
initial
unsafe
safe, under appropriate control
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Example: Aircraft Collision Avoidance
Two identical aircraft at fixed altitude & speed:
ud
uxv
uyvv
duy
x
dt
d
sin
cos
),,(xf
‘evader’ (control) ‘pursuer’ (disturbance)
x
y
uv
d
v
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Continuous Reachable Set
x
ySolve:
Display: G(t) = fx : J (x;t) < 0g
à @t@J (x;t) =minf0;maxumindH(x; @x
@J (x;t);u;d)g
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Collision Avoidance Filter
Simple demonstration– Pursuer: turn to head toward evader– Evader: turn to head right
pursuer
safety filter’s input modification
pursuer’s inputevader’s desired input
evader
evader’s actual input
unsafe setcollision set
Movies …
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Blunder Zones for Closely Spaced Approaches
evader
EEM Maneuver 1: accelerateEEM Maneuver 2: turn 45 deg, accelerate
EEM Maneuver 3: turn 60 deg
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Blunder Zone is shown by the yellow contour
Red Zone in the green tunnel is the intersection of the BZ with approach path.
The Red Zone corresponds to an assumed 2 second pilot delay. The Yellow Zone corresponds to an 8 second pilot delay
Implementation: Display design courtesy of
Chad Jennings, Andy Barrows, David Powell
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Map View showing a blunder
The BZ calculations are performed in real time (40Hz) so that the contour is updated with each video frame.
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Verified Mode Switching in Autopilots
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Use in Cockpit Interface Verification• Controllable flight envelopes for landing and Take Off / Go
Around (TOGA) maneuvers may not be the same• Pilot’s cockpit display may not contain sufficient information to
distinguish whether TOGA can be initiated
flareflaps extendedminimum thrust
rolloutflaps extendedreverse thrust
slow TOGAflaps extended
maximum thrust
TOGAflaps retracted
maximum thrust
flareflaps extendedminimum thrust
rolloutflaps extendedreverse thrust
TOGAflaps retracted
maximum thrust
revised interface
existing interface
controllable flare envelope
controllable TOGA envelope
intersection
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V1
V2
V3 V4
Communication Zone
Safety Assurance Zone
V1
V2
V3 V4
A More General Problem Structure
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Neighborhood of ith vehicle
(Decomposed) Centralized Optimization
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Fixed time horizon – complete global map
t à 2ts t à ts t t + T à 2ts t + T à ts t + T
Bargainingstart
fixed time horizon
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Flight Plans published by aircraft 1
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Another Example
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Flight Plans published by aircraft 1
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Receding horizon – incomplete global map
t à 2ts t à ts t t + T à 2ts t + T à ts t + T
moving time horizon
tsBargainingstart
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• Constraints embed:local dynamics: coordinated turn and straight flight [hdi
]
input constraints: limited turn rate and velocity [gei]
global coordination constraints: minimum safety assurance [gsij] for all j within
neighborhood of i
Local Optimization with Constraints
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Centralized Optimization
Decomposed Centralized Optimization
Decomposition I
Pareto optimalityNash equilibrium
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Define Hamiltonian for each subsystem:
is a Nash equilibrium for the centralized optimization problem if:
where
Thus, none of the subsystems can improve its solution, with all other subsystems’ solutions remaining fixed.
Nash Equilibrium for Centralized Problem
xã= [xã1; :::;x
ãi ; :::x
ãm]
H ci (x;õ;ö) = fi(xi) +õTh(x) +öTg(x)
H ci (x
ã;õ;ö) ô H ci (x
ãi;õ;ö)
xãi = [xã1; :::;xi; :::xã
m]
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Decomposed Centralized Optimization
Decentralized Optimization
Decomposition II
Nash equilibriumLocal optimal solutions
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Define Hamiltonian for each subsystem:
is a Nash equilibrium for the decentralized optimization problem if:
Optimal solutions by each of the subsystems
Nash Equilibrium for Decentralized Problem
xã= [xã1; :::;x
ãi ; :::x
ãm]
H dci (xi;õi;öijf xjgi) = fi(xi) +õT
i hi(xijf xjgi) +öTi gi(xijf xjgi)
H dci (x
ãi ;õi;öijf x
ãjg) ô H dc
i (xi;õi;öijf xãjg)
Proposition: is a Nash equilibrium of the centralized problem if and only if it is a Nash equilibrium of the
decentralized problem
xã
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Example: Nash Equilibrium at (0,0)
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• Global contraction function from the local optimization structures
• For a particular solution, local optimization of the ith
vehicle only affects the portion of F tied to its own local optimization
Using Penalty function methods
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• Eliminates cases in which a subsystem is artificially acting against a constraint dictated by another group
• Eliminates cases in which two subsystems act against each other with non-identical constraints
Cooperation Assumptions
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Multiple solutions, or “threads”, exist within the system:
Vehicle #1 Vehicle #2 Vehicle #3 Vehicle #4Iteration #1
Iteration #2
Iteration #3
Iteration #4
Iteration #4
Nash Bargaining with Multiple Threads
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Convergence Results
1. Global convergence to a (not necessarily feasible) Nash ‘solution’
2. If the gradients of the constraint functions are linearly independent (Linear Constraint Qualification Condition, LICQ), then global convergence to a feasible Nash solution
3. Pareto optimality for convex problems
[Inalhan, Stipanovic, Tomlin. Decentralized Optimization, with Application to Multiple Aircraft Coordination. CDC 2002, Submitted to JOTA]
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V1
V2
V3 V4
4-Vehicle Example
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PF i(xijf xjgi) = PF j(f xjgi;xi) 8j 2 N i
4-Vehicle Example
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Flight Plans published by aircraft 1
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Decentralized Initialization Procedure Heuristics– Multiple-Depots (Vehicles), Time-windows for access, Priority on
objectives and the vehicles– Iterative selection process carried at each vehicle– Best solution in the fleet is then selected from each vehicle’s
solution set
Applied to other problems of interest…
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Non-cooperativeFull information
CooperativeFull information
Cooperativeincomplete information
Non-cooperativeNo information
Lack of information Bounded Irrationality
Spectrum of Approaches
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Research Goals
• Design of provably correct and safe decentralized control protocols– Adapt to coordination– Allow for dynamic reconfiguration
• Treatment of information– Multi-scale provisioning of data based on inputs from various
sensing modalities– Urgency of the need for sensed data– Available bandwidth
• Verification algorithms– Used during design phase, to reduce the time spent during
the validation phase
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Stanford DragonFly UAV
10 ft wingspan
12 ft wingspan
[Jang, Teo, Inalhan, and Tomlin, DASC 2001], [Jang and Tomlin, AIAA GNC 2002]