Introduc tion River modeling NMPC NMHE Set invariance Conclusion s Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010 De Demer geregeld met MPC Public Doctoral Defense Toni Barjas Blanco Jur y: A. Haegemans, chair B. De Moor, promotor J. Berlamont, co- promotor J. Suykens P. Willems B. De Schutter (TU Delft) SCD Research Division ESAT – K. U. Leuven September 8 th , 2010
Public Doctoral Defense. De Demer geregeld met MPC. Jury:. A. Haegemans, chair B. De Moor, promotor J. Berlamont, co-promotor J. Suykens P. Willems B. De Schutter (TU Delft) R. Negenborn (TU Delft). Toni Barjas Blanco. SCD Research Division ESAT – K. U. Leuven September 8 th , 2010. - PowerPoint PPT Presentation
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Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
De Demer geregeld met MPCPublic Doctoral Defense
Toni Barjas Blanco
Jury:A. Haegemans, chair
B. De Moor, promotor
J. Berlamont, co-promotor
J. Suykens
P. Willems
B. De Schutter (TU Delft)
R. Negenborn (TU Delft)
SCD Research Division
ESAT – K. U. Leuven
September 8th, 2010
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
outline Introduction River Modeling Nonlinear Model Predictive Controller Nonlinear Moving Horizon Estimator Set Invariance Conclusions and Future research
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Introduction Floodings in the Demer basin
The damage caused in the Demer basin by the most recent floodings.
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Introduction
Current: three-position controller not based on rainfall predictions no optimization
In this research: “We implement a nonlinear
model predictive controller
for flood regulation.”
Goal: reduction of floods
Proposed control scheme
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling
Modeling techniques: Finite-difference models: very accurate, too complex Integrator-delay models: fast, linear System identification: not based on conservation laws
Reservoir model: Fast Nonlinear Accurate Conservation laws
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling
vgl, hgl
qbg
vopw, hopw
qman
qopw
K7 A
v1, h1
E
vs, hs
qK7
qA qE
vs2, hs2
qs
vs3, hs3
D
qs2 qs3
vvg, hvg
qhopw
vs4, hs4
qs4 qhs
vhopw, hhopw
qvs qD
q2
qzbopw qzb1
qgopw
qgafw
v3, h3
qvopw
hvopw
qK18
q3 q4
vw, hw
qK19 qK30
qbgopw
qK7lg
qgl
vbg, hbg
qK7bg
qzb3
q7 Demer
Zw
arte
wat
er
Zwartebeek
Vlootgracht
Schulensmeer
Webbekom
Her
k
Get
e
Beg
ijne
beek
Vel
pe
Leu
gebe
ek
Gro
te L
eigr
acht
Hou
wer
sbee
k
q6 q5
qK31
qzb2
v4, h4
vzb, hzb
qzw
q1
vlg, hlg
qK24B
hbgopw
qK24A
vzw, hzw
qzwopw
qhs2
v2, h2
qh
qsa
Discharges (q) Water levels (h) Volumes (v)
State variables : Inputs : Gates Rainfall-runoff (disturbances)
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling
Volume balance
Nonlinear H-V relation
0 in outV V q q
Conceptual model
h f V
inq outq,3inq
,1inq
,2inq,1outq
,2outq0V
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling Downstream reach
Nonlinear gate equations (Infoworks)
2
2
,0
b
up down
down down
qh h a
h h
vgl, hgl
qbg
vopw, hopw
qman
qopw
K7 A
v1, h1
E
vs, hs
qK7
qA qE
vs2, hs2
qs
vs3, hs3
D
qs2 qs3
vvg, hvg
qhopw
vs4, hs4
qs4 qhs
vhopw, hhopw
qvs qD
q2
qzbopw qzb1
qgopw
qgafw
v3, h3
qvopw
hvopw
qK18
q3 q4
vw, hw
qK19 qK30
qbgopw
qK7lg
qgl
vbg, hbg
qK7bg
qzb3
q7 Demer
Zw
arte
wat
er
Zwartebeek
Vlootgracht
Schulensmeer
Webbekom
Her
k
Get
e
Beg
ijne
beek
Vel
pe
Leu
gebe
ek
Gro
te L
eigr
acht
Hou
wer
sbee
k
q6 q5
qK31
qzb2
v4, h4
vzb, hzb
qzw
q1
vlg, hlg
qK24B
hbgopw
qK24A
vzw, hzw
qzwopw
qhs2
v2, h2
qh
qsa
up downh h
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling Nonlinear gate equations (Infoworks)
independent of the gate level uncontrollability (see later)
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling Calibration and validation
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling Calibration and validation
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
River modeling Calibration and validation
1
( ) ( )100
( )
nc iw
ri iw
y i y ie
y i
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Control scheme State of the art
Classical feedback and feedforwardOptimal controlHeuristic controlThree-position controlModel predictive control
Why model predictive control ? River dynamics are slow Constraint handling Rainfall predictions (model based) MIMO
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Control scheme Model predictive control
t
u
x
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
State estimation At each sampling time estimation current state based on past
measurements of a subset of the states.
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
State estimation State of the art in river control:
Sensor measurements Kalman filtering
Moving horizon estimation (MHE)
MHE: Dual of MPC Online constrained optimization problem Finite window in the past computational tractability Solves following problem: “Given the measurements of a subset of states
within the past time window, find all the states in that window that match the measurements as close as possible, given the underlying system model .”
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Setpoint regulation Simulation 2:
new K LTV based on 6 linear models 2 different step disturbances and no disturbance at the end
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Setpoint regulation
Simulation 3: simulation first 200 hours of 1998
LQR cost Robust feedback cost
2.6883 1.1913
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010
Conclusions and future research
A nonlinear model was determined accurate and fast enough for real-time control purposes. A nonlinear MPC and MHE scheme was developed that outperformed the current three-
position controller. Moreover, the scheme was robust against uncertainties. A new algorithm was developed for the efficient calculation of low-complexity polytopes. The
algorithm was used for improved setpoint regulation of the upstream part of the Demer.
Concluding remarks
Coupling control scheme with finite-difference model Extending model with flood map Distributed MPC Extend results to invariant low-complexity polytopes with a more general shape
Future research
Introduction River modeling NMPC NMHE Set invariance Conclusions
Toni Barjas Blanco - Public Doctoral Defense - September 8th, 2010