Degrees incorporating this pedagocial element :
Description
Feedback control design, diagnostic/supervision and process optimization typically require a specific modeling approach, which aims to capture the essential dynamics of the system while being computationally efficient. The first part of the class details the guiding principles that can be inferred from different physical domains and how multiphysics models can be obtained for complex dynamical systems while satisfying the principle of energy conservation. This leads to algebrodifferential mathematical models that need to be computed with stability and computational efficiency constraints. System identification constitutes the second part of the class, to include knowledge inferred from experimental data in the input/output map set by the model. It provides methods to evaluate the model performance, to estimate parameters, to design "sufficiently informative" experiments and to build recursive algorithms for online estimation.
Lesson  Topic  

1  Introduction to Modeling  
 Systems and models, examples of models, models for systems and signals.  
 PHYSICAL MODELING 

2  Principles of Physical Modeling  
 The phases of modeling, the mining ventilation problem example, structuring the problem, setting up the basic equations, forming the statespace models, simplified models.  
3  Some Basic Relationships in Physics  
 Electrical circuits, mechanical translation, mechanical rotation, flow systems, thermal systems, some observations.  
4  Bond Graphs:  
 Physical domains and power conjugate variables, physical model structure and bond graphs, energy storage and physical state, free energy dissipation, ideal transformations and gyrations, ideal sources, KirchhoffÂ’s laws, junctions and the network structure, bond graph modeling of electrical networks, bond graph modeling of mechanical systems, examples.  
 SIMULATION 

5  ComputerAided Modeling  
 Computer algebra and its applications to modeling, analytical solutions, algebraic modeling, automatic translation of bond graphs to equations, numerical methods  a short glance.  
6  Modeling and Simulation in Scilab  
 Types of models and simulation tools for: ordinary differential equations, boundary value problems, difference equations, differential algebraic equations, hybrid systems.  
 SYSTEM IDENTIFICATION 

7  Experiment Design for System Identification:  
 Basics of system identification, from continuous dynamics to sampled signals, disturbance modeling, signal spectra, choice of sampling interval and presampling filters.  
8  Nonparametric Identification:  
 Transientresponse and correlation analysis, frequencyresponse/Fourier/spectral analysis, estimating the disturbance spectrum.  
9  Parameter Estimation in Linear Models:  
 Linear models, basic principle of parameter estimation, minimizing prediction errors, linear regressions and least squares, properties of prediction error minimization estimates.  
10  System Identification Principles and Model Validation  
 Experiments and data collection, informative experiments, input design for openloop experiments, identification in closedloop, choice of the model structure, model validation, residual analysis.  
11  Nonlinear Blackbox Identification  
 Nonlinear statespace models, nonlinear blackbox models: basic principles, parameters estimation with GaussNewton stochastic gradient algorithm, temperature profile identification in tokamak plasmas  
 TOWARDS PROCESS SUPERVISION 

12  Recursive Estimation Methods  
 Recursive leastsquares algorithm, IV method, predictionerror methods and pseudolinear regressions, Choice of updating step  
 MODELING LABS 

Lab 12  Vibration Isolation for Heavy Trucks  
Lab 3  Modeling of a LEGO robot  
Lab 4  Modeling and Simulation of a Thermonuclear Plant  
Lab 5  Simulation and Control of an Inverted Pendulum Using Scilab  
Lab 6  Identification of an Active Vibration Control Benchmark Using Matlab  
Lab 78  Experiment design: Anthropogenic Impact on the Ozone Layer Depletion  
Lab 9  Recursive identification of a LEGO robot 
Bibliography
 L. Ljung and T. Glad, "Modeling of Dynamic Systems", Prentice Hall PTR, 1994.
 S. Stramigioli, "Modeling and IPC Control of Interactive Mechanical Systems: A Coordinatefree Approach", Springer, LNCIS 266, 2001.
 S. Campbell, JP. Chancelier and R. Nikoukhah, "Modeling and Simulation in Scilab/Scicos", Springer, 2005.
 L. Ljung, "System Identification: Theory for the User", 2nd Edition, Information and System Sciences, (Upper Saddle River, NJ: PTR Prentice Hall), 1999.
 O. Hinton, "Digital Signal Processing", Chapter 6  Describing Random Sequences, EEE305 class material, 2003.