Program of the 1st Spring Doctoral School on

Data-driven Model Identification of Dynamical Systems

Nancy, France April, 3-6, 2017

 

The 4-days Spring Doctoral School will consist of a series of lectures followed by computer exercises.

For the computer exercices, it is required that participants bring their own laptop with Matlab (version at least R2016a) installed with stand alone license.

Final program table

April 3, 2017 - 8h30-9h00
Registration - Hall of Building F - Polytech Nancy

    April 3, 2017 - 9h00-17h00
    Lecturer: Prof. Lennart Ljung (Linköping University, Sweden)

      2-day course on System Identification: A Prediction Error Perspective

      Overview

      • Theme 1: Overview of the problem area. Overall principles. Basic model structures. Bias and Variance. Model quality and uncertainty. Model validation.
      • Computer exercise 1. Getting hands on experience with real data. Simple models and model validation.
      • Theme 2: More on Linear models. Frequency domain data. Subspace Techniques. New approaches with regularization. High order, regularized simple models, advantages and disadvantages.
      • Computer exercise 2: Tests of subspace methods (N4SID, MOESP, CVA) and regularization. Experience of choice of model orders and auxiliary variables.

      April 4, 2017 - 8h30-16h30
      Lecturer: Prof. Lennart Ljung (Linköping University, Sweden)

      • Theme 3: Non-linear model structures. Wiener and Hammerstein models, Neural Networks. Grey box models with physical structure.
      • Computer exercise 3: Hands on identification of non-linear models.
      • Theme 4: Special questions. Experiment design. Concluding remarks.

       

       

      April 5, 2017 - 8h30-17h00
      Lecturer:  Prof. Hugues Garnier (University of Lorraine, France)

      Data-driven approaches for direct continuous-time model identification

      Overview

        • Theme 1: Overview of continuous-time linear model identification. Basic SVF-based and optimal instrumental variable (IV)-based estimators. Practical and software aspects.
        • Computer exercise 1. Getting hands on experience with the CONTSID toolbox methods with real data.
        • Theme 2: Benefits for practical applications. IV extensions to advanced situations (MISO systems, time-delay systems, simple process models, LTV systems,...).
        • Computer exercise 2: Getting hands on experience on advanced situations.

         

        April 6, 2017 -  8h30-12h15
        Lecturer: Prof. Marion Gilson (University of Lorraine, France)

        Frequency-domain, closed-loop, Hammerstein and LPV model identification

        Overview

            • Theme 1: Frequency-domain identification. Closed-loop system identification. Instrumental variable-based techniques.
            • Computer exercise 1: Hands on frequency-domain and closed-loop model identification.
            • Theme 2: Hammerstein and Linear Parameter Varying (LPV) model identification.
            • Computer exercise 2: Hands on identification of Hammerstein and LPV models.

            April 6, 2017 -  13h30-17h00
            Lecturer: Mathieu Cuenant (MathWorks)

            Identification and controls - An industrial perspective