Modelling and identification of physical systems work-group

In many fields, a reliable model of the physical system under study is required. Modeling a system is theoretically feasible by using exclusively a white-box model obtained by combing the laws of physics governing the behavior of the process. By definition, this prodecure requires high-level skills in many different fields that could be difficult to afford and may lead to complex and non-parsimonious models. System identification or experimental modeling is an interesting solution for the estimation of physical systems because such a procedure combines prior information and experimental results directly extracted from the plant to identify. The resulting model is often qualified as gray-box model. Whatever the structure of the model (linear, non-linear, linear parameter-varying), the members of this group aim at estimating a behavioral model of the process. They deal with methodological aspects of physical parameter estimation, reconstruction of the input signals and control oriented system identification. Studies are carried out for both monovariable and multivariable systems, either in an open or a closed loop configuration. The developed tools are applied to several types of processes (electrical engineering, thermics, robotics, …) and are also used in other groups of the department.

The main issues presently considered are:

  • identification of linear and non-linear continuous-time systems; 
  • development, improvement and initialization of the output-error methods; 
  • interaction between continuous-time and discrete time identification and models; 
  • development and improvement of subspace-based identification; 
  • closed-loop and open-loop identification; 
  • identification with prior-information; 
  • re-parameterization of continuous-time and discrete time models; 
  • parameter estimation of linear time invariant, time variant and parameter-varying models.

The main application fields presently considered are:

  • electrical engineering; 
  • aeronautics and transport; 
  • robotics; 
  • thermal energy and energetics; 
  • wastewater treatment.