Modelling, Identification and Estimation

The system identification is probably the most important and difficult step required for a successful modern control design. The Course is aimed at engineers who are involved in system modeling and model based control/simulation. Basic System ID methods such as least square algorithm and Kalman filter estimation are discussed in this course to provide a good background understanding. Real life issues such as implementing system ID and model validation can be problematic and this will be addressed and discussed in the course. Furthermore, two popular System ID techniques namely parameter estimation for grey-box models and nonlinear system modelling are illustrated as well. Lastly, the application of artificial neural network to identify and/or approximate a static and dynamic model is demonstrated too.

All presented topics will be supported by practical engineering examples and will arm trainees with efficient approach to tackle real-life problems. Lectures and Hands-On sessions will provide a methodology and step-by-step guide of using all presented algorithms in their engineering practice.