School / Prep
ENSEIRB-MATMECA
Internal code
EE8AU214
Description
The problem addressed concerns the determination of an analytical model belonging to a given class, based on the knowledge of input/output signals. The dynamic behavior predicted by the model must be as close as possible to that of the process under consideration, in the sense of a criterion. There are two main classes of identification methods: non-parametric and parametric. In this course, we focus on parametric methods. Any identification procedure proceeds as follows: Choice of experimental protocol, choice of model structure, choice of estimation method, validation of the estimated model. The aim of this course is to cover these different stages. More specifically, we begin with a presentation and critical analysis of the different structures used in parametric estimation. Then, among the various estimation methods available in the scientific literature, the ordinary least squares method, based on the minimization of a quadratic criterion, is discussed. The various validation tests are then presented. The course concludes with a chapter on the prediction error method.
Teaching hours
- CIIntegrated courses8h
- CMLectures10h
- TIIndividual work8h
Mandatory prerequisites
Linear system dynamics, Sampled systems, Discrete control law synthesis, Digital signal processing.
Syllabus
The course content is as follows: * Introduction and motivation: Presentation of objectives, review of non-parametric methods, definitions of auto and inter-correlation functions, power spectral densities and the theory of random variables. * Model structure in identification: Presentation of ARX, ARMA, ARMAX, OE and Box-Jenkins structures. Analysis of advantages and disadvantages. * Parametric estimation using ordinary least squares. Presentation of the problem, quadratic criterion, criterion minimization. * Quantifying estimation confidence: Prediction error whiteness test, Input/output correlation tests, Confidence regions in parametric space, in the Bode and pole/zero planes. * Prediction error method. Problem presentation and resolution. * Design office: Application of method tools to the case of a real process.
Further information
Automatic
Assessment of knowledge
Initial assessment / Main session - Tests
Type of assessment | Type of test | Duration (in minutes) | Number of tests | Test coefficient | Eliminatory mark in the test | Remarks |
---|---|---|---|---|---|---|
Integral Continuous Control | Continuous control | 1 |