Estimation and Filtering

Lecturer:

Tutor:

Supervisor:

Department of Control Engineering

Synopsis:

The objective of the subject is to introduce parametr estimation and state filtering methods from a unified bayesian viewpoint. Methods for the estimation of parameters of ARX models and filtering of state of a dynamic system including implementation and numerically robust algorithms, as well as Monte Carlo methods, are studied in details. Also, basic fault detection and isolation methods based on multiple models are introduced.

Requirements:

Syllabus of lectures:

1. Introduction

2. Estimation and filtering - problem formulation

3. One-shot and recursive parameter estimation for constant parameters

4. Tracking of time-varying parameters, forgetting

5. Robust numerical implementation of presented algorithms

6. Utilization of prior information, parallel and alternative models

7. Stochastic system, basic properties

8. Kalman filter, problem formulation, basic properties

9. Kalman filter for non-white process/measurement noise

10. Extended Kalman filter, applications

11. Filtering, prediction and smoothing

12. Nonlinear estimation and filtering

13. Monte Carlo implementation, sampling/resampling algorithms

14. Applications

Syllabus of tutorials:

1. - 3. Numerical implementation of algorithms for recursive identification

4. - 6. Properties of algorithms and forgetting methods

7. - 9. Numerical implementation of Kalman filter

10. - 11. Properties of Kalman filter

12. - 14. Algorithms for nonlinear estimation

Study Objective:

Study materials:

[1] Lewis, F.L.: Optimal Estimation. J.Wiley and Sons, N.Y. 1986, 1993

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: