Automatic, Nonlinear Systems, Observability and Observer Design, Control and Stabilization
The research topic of the SNLEP team concerns the theorical development and implementation of tools devoted to: Analyze, Model, Simulate, Control and Optimize Systems in Process Engineering.
Our theoretical developments are motivated by practical applications encountered through collaborations with industrial and academic partners. More specifically, our work focuses on model design and reduction, parametric identification and observation, diagnosis and control.
- The Modeling concerns polymerization crystallization freeze and bio- processes. We exploit mainly heat balance and matter conservation law, heat and matter diffusion, mass transfer between phases and population balance in particulate processes to develop models suitable for simulation, observation and control.
- Identification exploits tools coming from model predictive control and refers to experimental design in order to facilitate online parameter identification.
- The observation topic refers to the design of algorithm to estimate online parameters and unknown quantities of the model.
- Fault diagnosis refers to the detection of fault which is an unpermitted deviation of at least one characteristic property of the process from the acceptable, usual, standard condition.
- The control topic focuses on the stabilization problem for finite and infinite dimensional systems, the tracking and control problem using optimization tools.
The chemical processes mainly concerned by the SNLEP team are polymerization processes, crystallization processes and drying processes.
Director of SNLEP: Hassan Hammouri
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Équipes de recherche
- Yang Yu, Hai-Long Pei, Cheng-Zhong Xu. Identification of Water Depth and Velocity Potential for Water Waves. Systems and Control Letters, Elsevier, 2019, ⟨10.1016/j.sysconle.2018.12.010⟩. ⟨hal-02056751⟩
- Toufik Bakir, Bernard Bonnard, Sami Othman. Predictive control based on nonlinear observer for muscular force and fatigue model. ACC 2018 – The 2018 American Control Conference, Jun 2018, Milwaukee, United States. pp.2157-2162, ⟨10.23919/ACC.2018.8430962⟩. ⟨hal-01591187v2⟩
- Nawel Afsi, Toufik Bakir, Sami Othman, Anis Sakly. Model-free control of a seeded batch crystallizer. Canadian Journal of Chemical Engineering, Wiley, 2018, 96 (6), pp.1306 – 1316. ⟨10.1002/cjce.23066⟩. ⟨hal-01901734⟩
- H. Hammouri, S. Ahmed, Sami Othman. Observer design based on immersion technics and canonical form. Systems and Control Letters, Elsevier, 2018, 114, pp.19 – 26. ⟨10.1016/j.sysconle.2018.02.003⟩. ⟨hal-01901760⟩
- Estelle Lebègue, Carole Farre, Catherine Jose, Joëlle Saulnier, Florence Lagarde, et al.. Responsive Polydiacetylene Vesicles for Biosensing Microorganisms. Sensors, MDPI, 2018, 18 (2), pp.599. ⟨10.3390/s18020599⟩. ⟨hal-01740158⟩
- Ngoc-Tu Trinh, Vincent Andrieu, Cheng-Zhong Xu. Output regulation for a cascaded network of 2 × 2 hyperbolic systems with PI controller. Automatica, Elsevier, 2018, 91, pp.270-278. ⟨10.1016/j.automatica.2018.01.010⟩. ⟨hal-02056784⟩
- Pauline Bernard, Laurent Praly, Vincent Andrieu, Hassan Hammouri. On the triangular canonical form for uniformly observable controlled systems. Automatica, Elsevier, 2017, 85, pp.293 – 300. ⟨10.1016/j.automatica.2017.07.034⟩. ⟨hal-01643874⟩
- Mohamed Abdelmoula Benlahrache, Sami Othman, Nida Sheibat-Othman. Multivariable model predictive control of wind turbines based on Laguerre functions. Wind Engineering, Multi-Science Publishing, 2017, 41 (6), pp.409 – 420. ⟨10.1177/0309524X17721997⟩. ⟨hal-01901756⟩
- Mohamed Benlahrache, Khaled Laib, Sami Othman, Nida Sheibat-Othman. Fault Tolerant Control of Wind Turbine Using Robust Model Predictive Min-Max approach. IFAC-PapersOnLine, Elsevier, 2017, 50 (1), pp.9902 – 9907. ⟨10.1016/j.ifacol.2017.08.1622⟩. ⟨hal-01901768⟩
- Mohamed Benlahrache, Sami Othman, Nida Sheibat-Othman. Multivariable model predictive control of wind turbines in presence of actuator fault. 2017 8th International Renewable Energy Congress (IREC), Mar 2017, Amman, France. ⟨10.1109/IREC.2017.7926034⟩. ⟨hal-01901791⟩