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Controlling Large-Scale Systems with Distributed ModelPredictive Control
James B. Rawlings
Department of Chemical and Biological Engineering
November 8, 2010Annual AIChE Meeting
Salt Lake City, UT
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Outline
1 Overview of Distributed Model Predictive ControlControl of large-scale systems
2 Cooperative ControlStability theory for cooperative MPC
3 Conclusions and Future Outlook
4 Some Comments on Tom Edgar
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Model predictive control
What are the goals of MPC?
Choose inputs whichbring outputs to theirsetpoints
Minimize objectivefunction over N futuresteps
← Past
Inputs
Setpoint
Future →
u
k = 0
yOutputs
minu
V (x ,u) =N−1∑k=0
`(x(k), u(k)) + Vf (x(N))
subject to
x+ = Ax + Bu
y = Cx
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Chemical plant integration
Material flow
Energy flow
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Ideal plantwide MPC
minu1,u2
V (u1,u2)
MPC
u1 u2
Ideal controllerI perfect modelI never goes offlineI optimizes infinitely fastI samples infinitely fast
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Practical plantwide MPC
u1 u2
MPC 1 MPC 2
minu1
V1(u1) minu2
V2(u2)
Realistic controllerI approximate modelI MPCs fail or require maintenanceI finite optimization timeI multiple sampling rates
Goal: make realistic controller close to ideal controller
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Plantwide distributed MPC
u1 u2
MPC 1 MPC 2
minu1
V (u1,u2) minu2
V (u1,u2)
Decentralized controlI no communicationI not stable for strongly interacting subsystems
Noncooperative controlI use full modeling informationI not stable for strongly interacting subsystems
Cooperative controlI use same objective in each controllerI stability independent of interaction strength
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Cooperative model predictive control
u2
u1
u0
V (u1,u2)
MPC 1 MPC 2
u∗1
u∗2
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Plantwide suboptimal MPC
u2
u1
u0
u1
u2
V (u1,u2)
Early termination of optimization gives suboptimal plantwide feedback
Use suboptimal MPC theory to prove stability
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Plantwide suboptimal MPC
Consider closed-loop system augmented with input trajectory(x+
u+
)=
(Ax + Bug(x ,u)
)Function g(·) returns suboptimal choice
Stability of augmented system is established by Lyapunov function
a |(x ,u)|2 ≤ V (x ,u) ≤ b |(x ,u)|2
V (x+,u+)− V (x ,u) ≤ −c |(x , u)|2
Adding constraint establishes closed-loop stability of the origin for allu1
|u| ≤ d |x | x ∈ Br , r > 0
Cooperative optimization satisfies these properties for plantwideobjective function V (x ,u)
1(Rawlings and Mayne, 2009, pp.418-420)Rawlings Distributed MPC 10 / 23
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LV control of distillation column
MPC 1MPC 2
S
xDR
xB
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LV control of distillation column
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0 10 20 30 40 50
xD
Time (min)
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50
xB
Time (min)
0
0.05
0.1
0.15
0.2
0.25
0.3
0 10 20 30 40 50
S
Time (min)
CentCoop (1 iter)
NoncoopDecent
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 10 20 30 40 50
R
Time (min)Rawlings Distributed MPC 12 / 23
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LV control of distillation column
Performance comparison
Cost Performance loss (%)
Centralized MPC 75.8 0Cooperative MPC (10 iterates) 76.1 0.388Cooperative MPC (1 iterate) 87.5 15.4Noncooperative MPC 382 404Decentralized MPC 364 380
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Plantwide topology
Plant subsystems can often be grouped spatially or dynamically
Neighborhoods of subsystems naturally arise from topology
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Traditional hierarchical MPC2
Coordinator
MPCMPC MPC
1s 1s5s 3s 0.5s
Setpoints
2min1min
1hr
Data
Plantwide coordinator
Coordinator
MPC MPC
Multiple dynamical time scales in plant
Data and setpoints are exchanged on chosen scale
Optimization performed at each layer
2Mesarovic et al. (1970); Scattolini (2009)Rawlings Distributed MPC 15 / 23
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Cooperative MPC data exchange3
MPCMPC MPC
1s 1s5s 3s 0.5s
Data storageData storage
Read
Write
5s
MPC MPC
All data exchanged plantwide
Data exchange at each controller execution
3Venkat (2006); Stewart et al. (2010b)Rawlings Distributed MPC 16 / 23
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Cooperative hierarchical MPC4
MPCMPC MPC
1s 1s5s 3s 0.5s
Data storage
1min
Read
Write2min
1hr
Plantwide data storage
Data storage
MPC MPC
Optimization at MPC layer only
Only subset of data exchanged plantwide
Data exchanged at chosen time scale
4Stewart et al. (2010a)Rawlings Distributed MPC 17 / 23
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The challenge of nonlinear models
V (u1,u2)
u0
V (u1,u2)
u0
V (u1,u2)
u0
V (u1,u2)
u0
V (u1,u2)
u0 u1
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Distributed gradient projection - example
0
1
2
3
4
0 1 2 3 4
u2
u1
V (u1, u2) = e−2u1 − 2e−u1 + e−2u2 − 2e−u2
+1.1 exp(−0.4((u1 + 0.2)2 + (u2 + 0.2)2))
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Conclusions
Cooperative MPC theory maturing (Stewart et al., 2010b; Maestre et al.,
2010)
Satisfies hard input constraints
Provides nominal stability for plants with even strongly interactingsubsystems
Retains closed-loop stability for early iteration termination
Converges to Pareto optimal control in the limit of iteration
Remains stable under perturbation from stable state estimator
Avoids coordination layer
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Future Outlook
Extensions required for practical implementation
Can we treat nonlinear plant models? Qualified yes.
Can we avoid coupled constraints? Qualified yes.
Can we reduce the assumed complete communication? Yes.
Can we accommodate time-scale separation? Yes.
Can we nest layers within layers? Yes.
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Personal observations of Tom
He’s oldI took optimization from Tom when I was an undergraduate!I did research with Tom when I was an undergraduate!My son is now doing research with Tom as an undergraduate!
He’s wilyI’ve played a lot of golf with Tom over the years at research meetings,and I want to make it clear he cheats!Tom was department chair when I joined the University of Texas asan assistant professor. He was a great mentor.Tom founded the Texas Modeling and Control Consortium in 1993;The Texas Wisconsin Modeling and Control Consortium in 1995;The Texas Wisconsin California Control Consortium in 2007.
He’s a lot of fun to be around
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Further reading I
J. M. Maestre, D. Munoz de la Pena, and E. F. Camacho. Distributed model predictivecontrol based on a cooperative game. Optimal Cont. Appl. Meth., In press, 2010.
M. Mesarovic, D. Macko, and Y. Takahara. Theory of hierarchical, multilevel systems.Academic Press, New York, 1970.
J. B. Rawlings and D. Q. Mayne. Model Predictive Control: Theory and Design. NobHill Publishing, Madison, WI, 2009. 576 pages, ISBN 978-0-9759377-0-9.
R. Scattolini. Architectures for distributed and hierarchical model predictive control - areview. J. Proc. Cont., 19(5):723–731, May 2009. ISSN 0959-1524.
B. T. Stewart, J. B. Rawlings, and S. J. Wright. Hierarchical cooperative distributedmodel predictive control. In Proceedings of the American Control Conference,Baltimore, Maryland, June 2010a.
B. T. Stewart, A. N. Venkat, J. B. Rawlings, S. J. Wright, and G. Pannocchia.Cooperative distributed model predictive control. Sys. Cont. Let., 59:460–469, 2010b.
A. N. Venkat. Distributed Model Predictive Control: Theory and Applications. PhDthesis, University of Wisconsin–Madison, October 2006. URLhttp://jbrwww.che.wisc.edu/theses/venkat.pdf.
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