Transcript Diapositive 1

Model Predictive Control in
simulation or on line of a
continuous (time) process :
use of the MPC@CB1
control software
1©
Université Claude Bernard Lyon 1 – EZUS, january 2007
To use MPC@CB, please contact his author:
[email protected]
http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008
http://www.lagep.univ-lyon1.fr/signatures/dufour.pascal
MPC: the idea (born in the ‘70)
Morari, M.; Lee, J. L. Model predictive control: past, present and future.
Computers and Chemical Engineering 1999, 23, 667–682
Model prediction
Reference
Past
Futur
Process
Control
Present : k
k+Nc
k+Np
MPC@CB: what for ?
• It consists in sources files that may be used with Matlab to realize the
predictive control under constraints of a (time) continuous process.
• Theses codes may be easily adapted for any SISO (Single Input Single
Output) process, through user defined files synchronized by few
standards main files. The model has to be given as:
 x  f ( x, u )

 y  g ( x)
that is: the SISO model features any number of states variables, may be
linear or not linear, time variant or time invariant, based on PDE and/or
ODE. The simulated process equations and the model equations may be
different.
MPC@CB: for which
control problem?
It is simple for the user to specify one of the MPC problem:
• Regulation problem, trajectory tracking, operating time
minimization, with or without output.
• In order to study the MPC robustness, modeling uncertainties
(in equations and/or parameters) may be introduced between
the simulated process and the model used in the MPC.
• A cascaded process may be specified in process output.
• Any ending condition may be specified to finish the run.
• Open loop control, PID may be used for performances
comparison.
MPC@CB: develop your own
next versions
The programming approach used for these codes allows to easily
develop your next versions of the source codes:
• MPC for any used defined constrained optimization problem
• Handle SIMO, MISO or MIMO model
• Handle observor (model based software sensor).
• Switch from simulation to real time application on the real process
• Develop your GUI
MPC@CB: references(*)
for the control law used
P. Dufour, Thèse "Contribution à la commande prédictive des systèmes à
paramètres répartis non linéaires", avec Y. Touré, directeur de thèse au
LAGEP Université Claude Bernard Lyon 1, 2000 OAI:TEL-00337724
P. Dufour, Y. Touré, D. Blanc, P. Laurent "On Nonlinear Distributed
Parameter Model Predictive Control Strategy: On-line Calculation Time
Reduction and Application to an Experimental Drying Process",
Computers and Chemical Engineering, 27(11), pp. 1533-1542, 2003.
OAI : HAL-00352371
(*) References may be uploaded as open archives from:
http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008
MPC@CB: references(*)
with previous applications
•
J. De Temmerman, P. Dufour, B. Nicolaï, H. Ramon, "MPC as control strategy for
pasta drying processes", soumis le 12 septembre 2007, Computers and Chemical
Engineering, 33(1), 50-57, 2009. OAI : hal-00350086
•
B. Da Silva, P. Dufour, N. Othman, S. Othman, « Model Predictive Control of Free
Surfactant Concentration in Emulsion Polymerization », submitted 6/21 September
2007 to the 17th IFAC World Congress 2008, Paper 823/1693, Seoul, South Korea,
July 6-11, 2008. OAI : hal-00352737
•
N. Daraoui, P. Dufour, H. Hammouri, « Model Predictive Control of the Primary
Drying Stage of the Drying of Solutions in Vials: an Application of the MPC@CB
Software (Part 1) », Proceedings of the 5th Asia-Pacific Drying Conference (ADC)
2007, vol. 2, pp. 883-888, Hong Kong, China, August ,13-15 2007. hal-00352431
•
K. Abid, P. Dufour, I. Bombard, P. Laurent, « Model Predictive Control of a Powder
Coating Curing Process: an Application of the MPC@CB© Software », Proceedings
of the 26th IEEE Chinese Control Conference (CCC) 2007, Zhangjiajie, China, vol. 2,
pp. 630-634, July 27-29 2007. OAI: hal-00338891
(*) References may be uploaded as open archives from:
http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008
MPC@CB: application 1:
powder coating curing (Abid et al., 2007)
 Output constraint, with a parameter error
PID (regulation)
MPC@CB (dynamic optimization + constraint)
MPC@CB: application 1:
powder coating curing (Abid et al., 2007)
 Output constraint, with a parameter error
PID (régulation)
MPC@CB (dynamic optimization + constraint)
Since the output constraint is
saturated, the control move
decreases
Conclusion : MPC@CB>PID :
• Operating time MPC  Operating time PID: - 10%
• Energy consumption with MPC  Energy consumption with PID: -6.72%
• Accuracy : MPC allows a better regulation than PID
MPC@CB: application 2:
vial lyophilisation (Daraoui et al., 2007)
Sublimation time
minimization
Maximize the
sublimation front
move H(t)
(Contraints on the input)
(Contraint on the output)
Ending condition: stop when H(t)=L
Sublimation Interface (m)
Sublimation Interface velocity (m/s)
MPC@CB: application 2:
vial lyophilisation (Daraoui et al., 2007)
-7
Velocity optimization
x 10
3
Signal optimised by the controller (top) and the related process output (bottom)
2
1
0
0
200
400
600
800
Time (min)
1000
1200
1400
0.015
End :
H(t)=L
0.01
0.005
0
0
200
400
600
800
Time (min)
1000
1200
1400
MPC@CB: application 2:
vial lyophilisation (Daraoui et al., 2007)
Since the output constraint is saturated,
the control move decreases
Output constraint
255
Buttom temperature (°K)
250
Process
Maximum allowed
245
240
235
230
0
200
400
600
800
Time (min)
1000
1200
1400
MPC@CB:
for you ?
In order to use the MPC@CB1 control software
for your problem, contact:
[email protected]
http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008
http://www.lagep.univ-lyon1.fr/signatures/dufour.pascal
1©
Université Claude Bernard Lyon 1 – EZUS, january 2007