Pascal DUFOUR

Maître de Conférences

  • Systèmes Non Linéaires et Procédés

Automatique des systèmes continus, commande prédictive, identification, estimation, commande de procédés.


Bureau 1 Campus de la Doua, 3 rue Victor Grignard, Ecole CPE, bât 308G, étage 3, LAGEP UMR 5007 G322 Villeurbanne 69622 France

Téléphone bureau 1: 04 72 43 18 78Fax bureau 1: 04 72 43 16 99


Site internet: http://sites.google.com/site/dufourpascalsite

Site internet: http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008

Site internet: http://scholar.google.com/citations?user=dsNXtcgAAAAJ

Bio

En recherche, je m’intéresse aux développements et aux applications d’outils théoriques de commande et d’identification paramétrique (notamment avec commande prédictive et observateur), et de logiciels fortement liés à des problèmes réels issus du génie des procédés. Ceci notamment dans des applications liées à des transferts de matière et de chaleur : séchage, lyophilisation, thermique, réaction catalytique, récupération d’énergie à bord de véhicules.

Concerning my research activities, I am interested in the developments and the applications of theoretical tools for control and parametric identification (based on model based predictive control and observer), and of software hardly dealing with problems in chemical engineering, especially for applications dealing with heat and mater transfer: drying, lyophilisation, thermal, catalysis reaction, waste heat recovery.

Optimal input design for parameter estimation of nonlinear systems: case study of an unstable delta wing

date:2017
références bibliographiques:

Qian, J., M. Nadri, and P. Dufour. “Optimal Input Design for Parameter Estimation of Nonlinear Systems: Case Study of an Unstable Delta Wing.” 90, no. 4 (2017): 873–87. https://doi.org/10.1080/00207179.2016.1225990.
Pages:873-887

International Journal of Control

 

Organic Rankine Cycle for Vehicles: Control Design and Experimental Results

date:2017
références bibliographiques:

Peralez, Johan, Madiha Nadri, Pascal Dufour, Paolino Tona, and Antonio Sciarretta. “Organic Rankine Cycle for Vehicles: Control Design and Experimental Results.” 25, no. 3 (May 2017): 952–65. https://doi.org/10.1109/TCST.2016.2574760.
Pages:952-965

IEEE Transactions on Control Systems Technology

 

State and Parameter Estimation: A Nonlinear Luenberger Observer Approach

date:2017
références bibliographiques:

Afri, Chouaib, Vincent Andrieu, Laurent Bako, and Pascal Dufour. “State and Parameter Estimation: A Nonlinear Luenberger Observer Approach.” 62, no. 2 (February 2017): 973–80. https://doi.org/10.1109/TAC.2016.2566804.
Pages:973-980

IEEE Transactions on Automatic Control

 

Organic Rankine Cycle for Vehicles: Control Design and Experimental Results

date:2016
références bibliographiques:

Peralez, Johan, Madiha Nadri, Pascal Dufour, Paolino Tona, and Antonio Sciarretta. “Organic Rankine Cycle for Vehicles: Control Design and Experimental Results.” , 2016, 1–14. doi:10.1109/TCST.2016.2574760.
Pages:1-14

IEEE Transactions on Control Systems Technology

 

State and Parameter Estimation: A Nonlinear Luenberger Observer Approach

date:2016
références bibliographiques:

Afri, Chouaib, Vincent Andrieu, Laurent Bako, and Pascal Dufour. “State and Parameter Estimation: A Nonlinear Luenberger Observer Approach.” , 2016, 1–1. doi:10.1109/TAC.2016.2566804.
Pages:1-1

IEEE Transactions on Automatic Control

 

Transient performance evaluation of waste heat recovery rankine cycle based system for heavy duty trucks

date:2016
références bibliographiques:

Grelet, Vincent, Thomas Reiche, Vincent Lemort, Madiha Nadri, and Pascal Dufour. “Transient Performance Evaluation of Waste Heat Recovery Rankine Cycle Based System for Heavy Duty Trucks.” 165 (March 2016): 878–92. doi:10.1016/j.apenergy.2015.11.004.
Pages:878-892

Applied Energy

 

Optimal control for an organic rankine cycle on board a diesel-electric railcar

date:2015
références bibliographiques:

Peralez, J., P. Tona, M. Nadri, P. Dufour, and A. Sciarretta. “Optimal Control for an Organic Rankine Cycle on Board a Diesel-Electric Railcar.” 33 (September 2015): 1–13. https://doi.org/10.1016/j.jprocont.2015.03.009.
Pages:1-13

Journal of Process Control

 

Observer Design for MIMO Non-Uniformly Observable Systems

The design of high gain observers is usually based on normal forms of observability. If the system is observable for every input (uniform observability), the gain of the observer does not required a solution to differential equation. For multiple input multiple output (MIMO) non-uniformly observable systems, we give here a sufficient condition that the input must satisfy in order to design an observer. Unlike uniformly observable systems, the observer gain of non-uniformly observable systems is derived from a Lyapunov differential equation.

date:2012
références bibliographiques:

Dufour, P., S. Flila, and H. Hammouri. “Observer Design for MIMO Non-Uniformly Observable Systems.” 57, no. 2 (2012): 511–16. doi:10.1109/TAC.2011.2166667.
Pages:511-516

IEEE Transactions on Automatic Control

 

Inferential MIMO predictive control of the particle size distribution in emulsion polymerization

A new inferential 2-step multiple input/multiple output (MIMO) model predictive control (MPC) of the particle size distribution (PSD) in emulsion polymerization processes is proposed. The bulk-like model describing the PSD is used with the material balances of initiator, radicals, monomer and surfactant. The inferential 2-step control strategy uses two measurements available online (without delay): the concentration of surfactant in the aqueous phase by conductimetry, and the concentration of monomer by calorimetry. In a first step, the optimal trajectory of surfactant concentration leading to the target PSD is calculated offline. In a second step, a multivariable model predictive control manipulates online the monomer and surfactant flow rates in order to track the precalculated surfactant concentration trajectory and to maximise the monomer concentration in the polymer particles in a constrained set-point tracking. Two control strategies are compared (nonlinear MPC and linearized MPC) with and without modelling errors.

date:2012
références bibliographiques:

Da Silva, Bruno, Pascal Dufour, Nida SHEIBAT -OTHMAN, and Sami Othman. “Inferential MIMO Predictive Control of the Particle Size Distribution in Emulsion Polymerization.” 38 (2012): 115–25. https://doi.org/10.1016/j.compchemeng.2011.11.003.
Pages:115-125

Computers & Chemical Engineering

 

Model predictive control during the primary drying stage of lyophilisation

During the primary drying stage of pharmaceutical solutions in vial, the sublimation front is the boundary between the dried and frozen layers that moves from the top of the vial to its bottom. While only few on-line measures are available, it is an important variable to control. This paper deals with the on-line partial differential equation model-based predictive control of the sublimation front position, assuming two strategies based on various availability of measurement used in the feedback loop. Through the MPC@CB control software, the robustness of the controller with respect to the main model parameter uncertainty is shown.

date:2010
références bibliographiques:

Daraoui, N., P. Dufour, H. Hammouri, and A. Hottot. “Model Predictive Control during the Primary Drying Stage of Lyophilisation.” 18, no. 5 (2010): 483–94. doi:10.1016/j.conengprac.2010.01.005.
Pages:483-494

Control Engineering Practice

 

Experimental predictive control of the infrared cure of a powder coating: A non-linear distributed parameter model based approach

This paper deals with the experimental model based predictive control of the infrared cure cycle of a powder coating. It is based on a dynamic infinite dimensional model of the cure in one spatial domain, which aims to represent the evolution of the temperature and the degree of cure during the cure under infrared flow. The sensitivity of this model with respect to the main radiative property is experimentally highlighted under open loop conditions. This partial differential equation model is then approximated in finite dimension in order to be used by the predictive controller. Since the sampling time is small (one second), a special model predictive control formulation is used here, which aims to decrease the on-line computational time required by the control algorithm. Experimental evaluation of this controller that is based on the MPC@CB software is then presented. For black and white paintings, the robustness of this control algorithm is shown during an experimental temperature constrained trajectory tracking, even under a strong modeling uncertainty. The conclusion of this study is that this controller may be used for advanced control of powder coating cure.

date:2010
références bibliographiques:

Bombard, I., B. Da Silva, P. Dufour, and P. Laurent. “Experimental Predictive Control of the Infrared Cure of a Powder Coating: A Non-Linear Distributed Parameter Model Based Approach.” 65, no. 2 (January 16, 2010): 962–75. doi:10.1016/j.ces.2009.09.050.
Pages:962-975

Chemical Engineering Science

 

MPC as control strategy for pasta drying processes

date:2009
références bibliographiques:

De Temmerman, J., P. Dufour, B. Nicolaï, and H. Ramon. “MPC as Control Strategy for Pasta Drying Processes.” 33, no. 1 (January 2009): 50–57. doi:10.1016/j.compchemeng.2008.06.004.
Pages:50-57

Computers & Chemical Engineering