Mpc@Cb Presentation En V2009 03 03

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Information about Mpc@Cb Presentation En V2009 03 03
Technology

Published on November 12, 2009

Author: dufourpascal

Source: slideshare.net

Description

MPC@CB presentation EN v2009-03-03

For more informations: http://MPC-AT-CB.univ-lyon1.fr

Model Predictive Control in simulation or on line of a continuous (time) process : use of the [email_address] 1 control software 1 © Université Claude Bernard Lyon 1 – EZUS, january 2007 To use MPC@CB, please contact his author: [email_address] http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008 http://www.lagep.univ-lyon1.fr/signatures/dufour.pascal

Model Predictive Control in simulation or on line of a continuous (time) process :

use of the [email_address] 1

control software

1 © Université Claude Bernard Lyon 1 – EZUS, january 2007

To use MPC@CB, please contact his author:

[email_address]

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) Reference k+Nc k+Np Process Model prediction Futur Past Control Present : k Morari, M.; Lee, J. L. Model predictive control: past, present and future. Computers and Chemical Engineering 1999, 23, 667–682

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: 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.

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:

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 .

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

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

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

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

PID (regulation) MPC@CB (dynamic optimization + constraint)  Output constraint, with a parameter error MPC@CB: application 1: powder coating curing ( Abid et al., 2007 )

PID (régulation)  Output constraint, with a parameter error MPC@CB (dynamic optimization + constraint) 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 Since the output constraint is saturated, the control move decreases MPC@CB: application 1: powder coating curing ( Abid et al., 2007 )

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

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 MPC@CB: application 2: vial lyophilisation ( Daraoui et al., 2007 )

End : H(t)=L MPC@CB: application 2: vial lyophilisation ( Daraoui et al., 2007 )

Since the output constraint is saturated, the control move decreases MPC@CB: application 2: vial lyophilisation ( Daraoui et al., 2007 )

In order to use the [email_address] 1 control software for your problem, contact: [email_address] 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 MPC@CB: for you ?

In order to use the [email_address] 1 control software

for your problem, contact:

[email_address]

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

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