formal synthesis of control strategies for dynamical ... · formal synthesis of control strategies...
TRANSCRIPT
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Formal Synthesis of Control Strategies for Dynamical Systems
Calin Belta
Tegan Family Distinguished Professor Mechanical Engineering, Systems Engineering,
Electrical and Computer Engineering
Boston University
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
A textbook problem in formal methods
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
Specification: “If x is set infinitely often, then y is set infinitely often.”
A textbook problem in formal methods
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
Specification: “If x is set infinitely often, then y is set infinitely often.”
Check if all the possible behaviors of the circuit satisfy the specification
A textbook problem in formal methods
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
Specification: “If x is set infinitely often, then y is set infinitely often.”
Formalization
Temporal Logic Formula
A textbook problem in formal methods
⇤♦x→⇤♦y
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
Model
Mathematical modeling
{x}{y}
∅ {x, y}
Formalization
Temporal Logic Formula
Specification: “If x is set infinitely often, then y is set infinitely often.”
A textbook problem in formal methods
⇤♦x→⇤♦y
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Process
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
Model
Mathematical modeling
Model checking (verification)
Formalization
Temporal Logic Formula
{x}{y}
∅ {x, y}
Specification: “If x is set infinitely often, then y is set infinitely often.”
A textbook problem in formal methods
⇤♦x→⇤♦y
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Process
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
A textbook problem in dynamics
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Process
Specification: “drive from A to B.”
A
B
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
A textbook problem in dynamics
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Process
Specification: “drive from A to B.”
A
B
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
A textbook problem in dynamics
Generate a robot control strategy
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Process
Specification: “drive from A to B.”
Model
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
Mathematical modeling
A
B
A textbook problem in dynamics
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Process
Specification: “drive from A to B.”
Model
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
Mathematical modeling
A
B
Formalization
Stabilization Problem: “make B an asymptotically stable equilibrium”
A textbook problem in dynamics
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Process
Specification: “drive from A to B.”
Model
S. Sastry – Nonlinear Systems: analysis, stability, and control, Springer, 1999
Mathematical modeling
A
B
Formalization
Stabilization Problem: “make B an asymptotically stable equilibrium”
Control
A textbook problem in dynamics
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Formal methods vs. dynamics
Process
Model
Specification “Drive from A to B.” “If x is set infinitely often, then y is set infinitely often.”
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Formal methods vs. dynamics
Process
Model
Specification
Simple
Complex
Complex
Simple
“Drive from A to B.” “If x is set infinitely often, then y is set infinitely often.”
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Need for formal methods in dynamical systems
x
y
z
photo
upload
upload
unsafe
extinguish
assist
Ulusoy, Belta, RSS 2013, IJRR 2014
Vehicle Control Strategy ?
Solution later in this talk
Spec: Off-line: “Keep taking photos and upload current photo before taking another photo. On-line: Unsafe regions should always be avoided. If fires are detected, then they should be extinguished. If survivors are detected, then they should be provided medical assistance. If both fires and survivors are detected locally, priority should be given to the survivors.”
NSF NRI
- Ideal controllers and sensors - Known map - Perfect localization
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Spec: Maximize the probability of satisfying: “Always avoid all and Visit , , , and infinitely often and should only be used for
Northbound travel and should only be used for Southbound travel. Uncertainty should always be below 0.9 m2 and when crossing bridges it should be below 0.6 m2.”
Need for formal methods in dynamical systems
Vehicle Control / Communication Strategies
?
Obstacle:!
River!
Obstacle:!Highway!
Obstacle:!
Highway!Obs!
Obstacle:!Highway!
Bridge!
2!
Bridge!
1!
Bridge!
1!
Marsh!
Plaza!Kenmore!Square!
Audubon!Circle!
Stadium!
NSF CMMI
ONR MURI
Map unknown environment
Localization and control Example later in the talk
- Noisy controllers and sensors - Unknown map - Probabilistic localization
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Need for formal methods in dynamical systems
NSF CPS
Coogan, Aydin Gol, Arcak, Belta, ACC 2015, IEEE TCNS 2016 Sadradini, Belta, ACC 2016, CDC 2016 Coogan, Arcak, Belta, ACC 2016, CSM 2017
• always the network is not congested • each queue at a junction will be actuated at least once every two minutes • whenever the number of vehicles on a link exceeds 40, within 3 min it should
decreases below 20
Traffic light and ramp meter control strategies ?
Example later in the talk
Spec:
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Need for formal methods in dynamical systems
Fuel Control System
[0,60) [9.7,59.7) 3 [0.1,59.7) 4 [0.5,59.7) 4F (( 0.875) ( 0.98) ( 0.29))G x G x G x< ∧ < ∧ >
i.e., “EGO is less than 0.875 for all times in between 9.7s and 59.7s and MAP is less than 0.98 for all times in between 0.1s and 59.7s and MAP is greater than 0.29 for all times in between 0.5s and 59.7s.
2. Supervised / unsupervised learning (good behavior)
1. Off-line / on-line data collection
3. Monitoring and anomaly mitigation (formal synthesis)
Not in this talk Jones, Kong, Belta, CDC 2014 Kong, Jones, Ayala, Gol, Belta, HSCC 2014 Bombara, Vasile, Penedo, Belta, HSCC 2016
DENSO Corporation, Japan
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Need for formal methods in dynamical systems
Haghighi, Jones, Kong, Bartocci, Grosu, Belta, HSCC 2015, CDC 2016 Not in this talk
1. Off-line / on-line data collection
2. Supervised / unsupervised learning (good behavior)
“Always, for each of the four ‘neighborhoods’, the power consumption level m is below 300 and the power consumption is below 200 in each of the neighborhoods’ quadrants at least once per hour. After 6 hours, the power consumption in all residential areas is above level 3.”
3. Monitoring and anomaly mitigation (formal synthesis)
ONR
Low Earth Orbit (LEO) satellites can gather temporal-spatial data (the figure shows the intense-traffic Strait of Gibraltar)
shutterstock.com
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Need for formal methods in dynamical systems
Not in this talk
NSF CPS Frontier
hiPS cells (Weiss lab)
Pattern formation using synthetic biology and micro-robotics
Formal methods approaches to pattern synthesis in biological systems - Pattern classifiers are learnt as formulae in spatial temporal logics with quantitative semantics - Parameters are determined through optimizations minimizing closeness to desired patterns
Vasculogenesis (Kamm lab)
Pattern formation using synthetic biology
NSF STC
pancreatic beta cells (Weiss and Hammond labs)
Simulation patterns in a Turing reaction diffusion system
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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(Fully-observable) nondeterministic (non-probabilistic) labeled transition systems with finitely many states, actions (controls), and observations (properties)
TL verification and control for finite systems
u1q
1q2
{π1}
u3
{π2,π
3}
{π1}
u4
u5
{π3,π
4}
q3
q4
u2
Finite system
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Linear Temporal Logic (LTL)
eventually always until
Syntax
♦π2 ♦⇤(π3 ∧ π4) (π1 ∨ π2)Uπ4
TL verification and control for finite systems
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Linear Temporal Logic (LTL)
Word:
Syntax
Semantics
eventually always until
♦π2 ♦⇤(π3 ∧ π4) (π1 ∨ π2)Uπ4
{π1}{π2,π3}{π3,π4}{π3,π4} · · ·
TL verification and control for finite systems
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Linear Temporal Logic (LTL)
Run (trajectory):
Word:
Syntax
Semantics
{π1}{π2,π3}{π3,π4}{π3,π4} · · ·
q1, q4, q3, q3, . . .
eventually always until
♦π2 ♦⇤(π3 ∧ π4) (π1 ∨ π2)Uπ4
u1q
1q2
{π1}
u3
{π2,π
3}
{π1}
u4
u5
{π3,π
4}
q3
q4
u2
TL verification and control for finite systems
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LTL verification (model checking)
Given a transition system and an LTL formula over its set of propositions, check if the language (i.e., all possible words) of the transition system starting from all initial states satisfies the formula.
SPIN, NuSMV, PRISM, …
TL verification and control for finite systems
q1
q2
{π1}
{π2,π
3}
{π1}
{π3,π
4}
q3
q4
π1Uπ4 False
(π1 ∨ π2)Uπ4 True
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TL verification and control for finite systems
Given a transition system and an LTL formula over its set of propositions, find a set of initial states and a control strategy for all initial states such that the produced language of the transition system satisfies the formula.
LTL control (synthesis)
u1q
1q2
{π1}
u3
{π2,π
3}
{π1}
u4
u5
{π3,π
4}
q3
q4
u2
Did not receive much attention until recently!
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State feedback control automaton
control
state
Rabin game!
TL verification and control for finite systems
Particular cases: - LTL without “eventually always”: Buchi game - LTL without “always” (syntactically co-safe LTL): the automaton is an FSA
u1q
1q2
{π1}
u3
{π2,π
3}
{π1}
u4
u5
{π3,π
4}
q3
q4
u2
LTL control (synthesis)
C. Bayer and J-P Katoen, Principles of Model Checking, MIT Press, 2008
C. Belta, B. Yordanov, and E. Gol, Formal Methods for Discrete-time Dynamical Systems, Springer, 2017
Optimal Temporal Logic Control for Finite Deterministic Systems Optimal Temporal Logic Control for Finite MDPs Temporal Logic Control for POMDPs Temporal Logic Control and Learning
Extensions
Svorenova, Cerna, Belta, IEEE TAC, 2015 Ding, Lazar, Belta, Automatica, 2014
Smith, Tumova, Belta, Rus, IJRR, 2011
Ding, Smith, Belta, Rus, IEEE TAC, 2014
Svoreňová, Leahy, Eniser, Chatterjee, Belta, HSCC 2015
Chen, Tumova, Belta, IJRR, 2013
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
Conservative TL control for small & simple dynamical systems
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Assume that in each region we can check for the existence of / construct feedback controllers driving all states in finite time to a subset of facets (including the empty set – controller making the region an invariant)
Conservative TL control for small & simple dynamical systems
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“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
Abstraction (bisimulation)
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Feedback
automaton
control
state
“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
Abstraction (bisimulation)
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Feedback
automaton
Refinement
control
state
Feedback controller
region
Feedback
hybrid
automaton
“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
Conservative TL control for small & simple dynamical systems
Abstraction (bisimulation)
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41
Control-to-facet Stay-inside Control-to-set-of-facets Control-to-face Stay-inside
Control-to-facet Stay-inside
polyhedral Library of controllers for polytopes
• checking for existence of controllers amounts to checking the non-emptiness of polyhedral sets in U • if controllers exist, they can be constructed everywhere in the polytopes by using simple formulas
C. Belta and L.C.G.J.M. Habets, IEEE TAC, 2006
M. Kloetzer, L.C.G.J.M. Habets and C. Belta, CDC 2006
L.C.G.J.M. Habets and J. van Schuppen, Automatica 2005
Control-to-set-of-facets
Conservative TL control for small & simple dynamical systems
Dynamics and partitions allowing for easy construction of bisimilar abstractions
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Conservative TL control for small & simple dynamical systems
“visit the green regions, in any order, while avoiding the grey regions”
♦q2 ∧ ♦q25 ∧⇤¬(q12 ∨ q13)
Control to one facet Deterministic quotient
Control to sets of facets Non-deterministic quotient
Initial states from which control strategies exist
Multi-affine dynamics
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Mapping complex dynamics to simple dynamics: I/O linearization
θ
dx
}{F
}{M
ε
dx
dx
θ
!
"
####
$
%
&&&&
=
cosθ
sinθ
0
!
"
###
$
%
&&&w1+
0
0
1
!
"
###
$
%
&&&w2
1
2
ww W
w
⎡ ⎤= ∈⎢ ⎥⎣ ⎦
x = u u U∈
1 Tw E R u
−=
1 0
0E
ε
⎡ ⎤= ⎢ ⎥⎣ ⎦
x = REw
J. Desai, J.P. Ostrowski, and V. Kumar. ICRA, 1998.
Conservative TL control for large & complex dynamics
Fully actuated point
U can be derived from W
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“Always avoid black. Avoid red and green until blue or cyan are reached. If blue is reached then eventually visit green. If cyan is reached then eventually visit red.”
Conservative TL control for large & complex dynamics
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Mapping complex dynamics to simple dynamics: differential flatness
Quadrotor dynamics • Nonlinear control system with 12 states (position, rotation, and their derivatives) with 4 inputs (total
thrust force from rotors and 3 torques) • Differentially flat with 4 flat outputs (position and yaw) • Up to four derivatives of the flat output and necessary to compute the original state and input
Mellinger and Kumar, 2011.; Hoffmann, Waslander, and Tomlin, 2008.; Leahy, Zhou, Vasile, Schwager, Belta, 2015
Conservative TL control for large & complex dynamics
http://sites.bu.edu/robotics/
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x
y
z
photo
upload
upload
unsafe
extinguish
assist
Global spec: “Keep taking photos and upload current photo before taking another photo. Unsafe regions should always be avoided. Local spec: If fires are detected, then they should be extinguished. If survivors are detected, then they should be provided medical assistance. If both fires and survivors are detected locally, priority should be given to the survivors.”
Persistent surveillance with global and local specs
Ulusoy, Belta, RSS 2013, IJRR 2014
Conservative TL control for large & complex dynamics
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Mission Specification: Time Window Temporal Logic (TWTL)
“Service site A for 2 time units within [0, 30] and site C for 3 time units within [0, 19]. In addition, within [0, 56], site B needs to be serviced for 2 time units followed by either A or C for 2 time units within [0, 10].”
Persistent surveillance with deadlines and resource constraints
Vasile, Aksaray, Belta, Theoretical Computer Science, 2017 (accepted)
Additional constraints: • operation time • charging time • timed temporal specs
Vasile and Belta, Robotics: Science and Systems (RSS) 2014
Conservative TL control for large & complex dynamics
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Persistent surveillance with deadlines and resource constraints
Vasile, Aksaray, Belta, Theoretical Computer Science, 2017 (accepted)
Vasile and Belta, Robotics: Science and Systems (RSS) 2014
Conservative TL control for large & complex dynamics
“Service site A for 2 time units within [0, 30] and site C for 3 time units within [0, 19]. In addition, within [0, 56], site B needs to be serviced for 2 time units followed by either A or C for 2 time units within [0, 10].”
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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polytopes X,Uxk+1 = Axk +Buk, xk ∈ X, u
k∈U
Problem Formulation: Find a set of initial states and a state-feedback control strategy such that all trajectories of the closed loop system originating there satisfy an scLTL formula over a set of linear predicates
U
X
p1
¬p1
p2
¬p2
p3
¬p3
Less conservative TL control for small and simple dynamics
Language-guided Approach: - Automaton-based partitioning and iterative refinement - Polyhedral Lyapunov functions used to construct polytope-to-polytope controllers - Solution is complete! (modulo linear partition and polyhedral Lyapunov functions)
E. Aydin Gol, M. Lazar, C. Belta., HSCC 2012, IEEE TAC 2014
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“Visit region A or region B before reaching the target T while always avoiding the obstacles”
Example
Less conservative TL control for small and simple dynamics
E. Aydin Gol, M. Lazar, C. Belta., HSCC 2012, IEEE TAC 2014
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Initial state: x0
Reference trajectories:
xr
0, xr
1. . .
ur
0, ur
1, . . .
Observation horizon : N
C(xk,uk) = (xk+N − xr
k+N)>LN (xk+N − xr
k+N)
+N�1X
i=0
�
(xk+i − xr
k+i)>L(xk+i − xr
k+i)
+ (uk+i − ur
k+i)>R(uk+i − ur
k+i)
,
X
U
Less conservative optimal TL control for small and simple dynamics
Optimal TL control
Standard Model Predictive Control (MPC, Receding Horizon)
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X
p1
¬p1
p2
¬p2
p3
¬p3
U
Initial state: x0
Reference trajectories:
xr
0, xr
1. . .
ur
0, ur
1, . . .
Observation horizon : N
C(xk,uk) = (xk+N − xr
k+N)>LN (xk+N − xr
k+N)
+N�1X
i=0
�
(xk+i − xr
k+i)>L(xk+i − xr
k+i)
+ (uk+i − ur
k+i)>R(uk+i − ur
k+i)
,
Problem Formulation: Find an optimal state-feedback control strategy such that the trajectory originating at satisfies an scLTL formula over linear predicates pix
0
Less conservative optimal TL control for small and simple dynamics
Optimal TL control
Language-guided MPC Approach: - Work on the refined automaton from the above TL control problem - Enumerate paths of length given by the horizon and compute the costs. - Terminal constraints ensuring the acceptance condition of the automaton: Lyapunov-
like energy function - Solve QP to find the optimal path
E. Aydin Gol, M. Lazar, C. Belta, Automatica 2015
Standard Model Predictive Control (MPC, Receding Horizon)
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O1
O2
T
A
B
N = 2 total cost = 29.688
N = 4 total cost = 0.886
N = 6 total cost = 5.12
Reference trajectory violates the specification Reference trajectory
Controlled trajectory
• “Visit region A or region B before reaching the target while always avoiding the obstacles”
• Minimize the quadratic cost with L=LN=0.5I2, R=0.2
Example
E. Aydin Gol, M. Lazar, C. Belta, Automatica 2015
Less conservative optimal TL control for small and simple dynamics
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Less conservative TL control for large and complex dynamics Iterative Partition vs. Sampling
Mission specification: “visit regions r1, r2, r3 and r4 infinitely many times while avoiding regions o1, o2, o3, o4 and o5”
Do (1) Partition (2) Construct region-to-region controller (3) Find controller for finite abstraction Until A solution is found
Do (1) Sample (2) Construct node-to-node controller (3) Find controller for finite abstraction Until A solution is found
Steve LaValle, 1998 Rapidly-exploring Random Trees (RRT) Rapidly-exploring Random Graphs (RRG) Karaman and Frazzoli, 2010
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Less conservative TL control for large and complex dynamics
Construct a transition system that contains a path satisfying the formula
1. LTL formula is translated to a Büchi automaton;
2. A transition system is incrementally constructed from the initial configuration using an RRG1-based algorithm;
3. The product automaton is updated incrementally and used to check if there is a trajectory that satisfies the formula
1S. Karaman and E. Frazzoli. IJRR , 2011.
Important Properties
● Probabilistically complete
● Scales incrementally (i.e., with the number of added samples at an iteration) - based on incremental Strongly Connected Component (SCC) algorithm 2
2Bernhard Haeupler, et al.. ACM Trans. Algorithms, 2012. C. Vasile and C. Belta. IROS 2013
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Less conservative TL control for large and complex dynamics
“Visit regions r1, r2, r3 and r4 infinitely many times while avoiding regions o1, o2, o3, o4 and o5”
Case study 1: 2D configuration space, 20 runs
Average execution time: 6.954 sec
Platform: Python2.7 on an iMac – 3.4 GHz Intel Core i7, 16GB of memory
Case study 2: 10-dimensional configuration space, 20 runs
Average execution time: 16.75 sec
“Visit 3 regions r1, r2, r3 infinitely often while avoiding obstacle o1
Case study 3: 20-dimensional configuration space, 20 runs
Average execution time: 7.45 minutes
“Visit 2 regions (r1, r2) infinitely often”
C. Vasile and C. Belta. IROS 2013
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Less conservative TL control for large and complex dynamics
• Global mission specification: “visit regions r1, r2, r3 and r4 infinitely many times while avoiding regions o1, o2, o3, o4 and o5”
• Local mission specification: “Extinguish fires and provide medical assistance to survivors, with priority given to survivors, while avoiding unsafe areas”
Fires and survivors are sensed locally. These service requests have given service radii.
Off-line part: generate a global transition system that contains a path satisfying the global spec
On-line (reactive) part: generate a local plan that does not violate the global spec
C. Vasile and C. Belta, ICRA 2014
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Spec: Maximize the probability of satisfying: “Always avoid all and Visit , , , and infinitely often and should only be used for
Northbound travel and should only be used for Southbound travel. Uncertainty should always be below 0.9 m2 and when crossing bridges it should be below 0.6 m2.”
Obstacle:!
River!
Obstacle:!Highway!
Obstacle:!
Highway!Obs!
Obstacle:!Highway!
Bridge!
2!
Bridge!
1!
Bridge!
1!
Marsh!
Plaza!Kenmore!Square!
Audubon!Circle!
Stadium!
- Noisy controllers and sensors - Unknown map - No GPS
Less conservative TL control for large and complex dynamics
Approach: • Generate a map of the unknown environment using purely vision and homography-based
formation control with multiple quadrotors • Label the map and define Gaussian Distribution Temporal Logic (GDTL) spec • Synthesize control policy using GDTL - Feedback Information RoadMaps (GDTL-FIRM) • Simultaneously track and localize the ground robot with a single aerial vehicle using a
homography – based pose estimation and position-based visual servoing control
E. Cristofalo, K. Leahy, C.-I. Vasile, E. Montijano, M. Schwager and C. Belta, ISER 2016. C. I. Vasile, K. Leahy, E. Cristofalo, A. Jones, M. Schwager and C. Belta, CDC 2016
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Map unknown environment
Localization and control
E. Cristofalo, K. Leahy, C.-I. Vasile, E. Montijano, M. Schwager and C. Belta, ISER 2016. C. I. Vasile, K. Leahy, E. Cristofalo, A. Jones, M. Schwager and C. Belta, CDC 2016
Less conservative TL control for large and complex dynamics
Spec: “Always avoid all and Visit , , ,
and infinitely often and should only be used for Northbound travel
and should only be used for Southbound travel. Uncertainty should always be below 0.9 m2 and when crossing bridges it should be below 0.6 m2.”
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Outline
TL verification and control for finite systems
Conservative TL control for small & simple dynamical systems
Conservative TL control for large & complex dynamical systems
Less conservative optimal TL control for small & simple dynamical systems
Less conservative TL control for large & (possibly) complex dynamical systems
Less conservative optimal TL control for large & simple dynamical systems
TL = Temporal Logic Limitation
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Signal Temporal Logic: Boolean (Qualitative) and Quantitative Semantics
Less conservative optimal TL control for large & simple dynamics
• Temporal operators are timed
• Semantics defined over signals
• Has qualitative semantics: real-valued function
Donze & Maler 2004, Fainekos et.al. 2009
ρ(s,φ)
Boolean: True Quantitative: 0.01
⇤[t1,t2](s ≤ 2.5) ♦[t3,t4](s > 3.5)
Boolean: False Quantitative: -0.2
Bemporad and Morari, 1999
⇤[t1,t2](s ≤ 2.5) ∧ ⌃[t3,t4](s > 3.5)
Boolean: False Quantitative: -0.2
• Boolean satisfaction of STL formulae over linear predicates can be mapped to feasibility of mixed integer linear equalities / inequalities (MILP feasibility)
• Robustness is piecewise affine in the integer and continuous variables
Raman et. al, 2014
Sadrardini & Belta, 2015
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Optimal STL Control
minuH
J(xH , uH)
subject to
Reduces to solving a MILP!
x+ = f(x, u)
xH, u
H satisfy STL formula over linear predicates
(any MLD system, e.g., piecewise affine)
(any linear cost)
dynamics
correctness
Less conservative optimal TL control for large & simple dynamics
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Planar Robot Example
x+= x+ u
ϕ = ⇤[40,50]A ∧ ⌃[0,40]⇤[0,10]B ∧ ⌃[0,30]C
Maximum robustness + Minimum fuel Minimum Fuel Only
H = 50
J = −1000ρ+H−1X
τ=0
�
�
�u[τ ]
�
�
�J =H−1X
τ=0
�
�
�u[τ ]
�
�
�
Sadraddini and Belta, Allerton Conference, 2015
Less conservative optimal TL control for large & simple dynamics
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STL Model Predictive Control (MPC)
Repetitive tasks in infinite time: global STL formulas:
⇤[0,∞]ϕ
uH [t] = argmin J(xH [t], uH [t])subject to x+ = f(x, u)
xH [t] |= ⇤[t−H,t]ϕ
Sadraddini and Belta,, 2015
J = ρ
J = �M(ρ� kρk) + Jc
J = Jc
M is a large number. When , effectively maximize ρ < 0 2Mρ
Terminal constraints are guaranteed!
xH(t) |= ϕ over H
Less conservative optimal TL control for large & simple dynamics
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x+=
✓
1 0.5
0 0.8
◆
x+
✓
0
1
◆
+ w
Example: Double Integrator
Minimize fuel consumption. If the spec becomes infeasible, maximize robustness. J =
t+H−1X
τ=t
�
�
�u[τ ]
�
�
�c
⇤[0,∞]
�
⌃[0,4]((x1 ≤ 4) ∧ (x1 ≥ 2)) ∧ ⌃[0,4]((x1 ≥ −4) ∧ (x1 ≤ −2))�
Spec:
Sadraddini and Belta 2015
Less conservative optimal TL control for large & simple dynamics
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⇤[0,∞]ϕ
Takes less than 5 sec. to compute a optimal robust control strategy (MILP in 212 dimensions)
Cost: delay over a given horizon
Congestion free If density ever reaches 3, then in 3
minutes should become less than 3
Mixed urban and freeway traffic network with 53 links, 14 intersections controlled by traffic lights, 4 ramp meters
Spec:
Model: discrete-time piecewise affine system
Example: Traffic network
Sadraddini and Belta 2016
Less conservative optimal TL control for large & simple dynamics
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Summary
• Automata (Buchi, Rabin) games can be adapted to produce conservative TL control strategies for simple and small dynamical systems
• The above can be extended to conservative strategies for large and complicated systems by using I/O linearization techniques
• Partition refinement can be used to reduce conservatism for simple and small dynamical systems -> connection between optimality and TL correctness
• Sample-based techniques can be used to generate probabilistically complete TL strategies in high dimensions
• TL with quantitative semantics can be used for robust, provably-correct optimal control in high dimensions
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Acknowledgements
Jana Tumova (now at KTH)
Ebru Aydin Gol (now at Google)
Alphan Ulusoy (now at Mathworks)
Marius Kloetzer (now at UT Iasi)
Cristian Vasile (now at MIT)
Sadra Sadradini
Kevin Leahy (now at Lincoln Lab, MIT)
Austin Jones (now at Lincoln Lab , MIT)
Derya Aksaray (now at MIT)