5th - 6th August 2009, Glasgow
This meeting has since been held. (Register your interest for the next course here.)
Some of the presentations may now be available for download on-line (ACTC members only). You can do so from our Download Centre or simply by clicking on the appropriate link in the agenda below.
Please Note: Presentation material downloaded from our web-site should not be incorporated (either in part or in entirety) into other work etc., nor distributed (either in part or in entirety) to third parties, without the express permission of the authors.
Employees of ACTC member companies, are entitled to two places free of charge. Employees of companies that are not ACTC members will be required to pay a nominal fee to help defray costs
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
The course will be held in The Premier Travel Inn, George street, Glasgow (map and travel directions).
Glasgow City Centre offers a wide range of accommodation, you can find our recommendations here.
|09.30||System Identification for Linear Systems|
|11.15||Hands-on: System ID with Least Squares Algorithm|
|12.00||Hands-on: System ID with a Kalman Filter to Estimate an Offset|
|13.30||System Identification Implementation Issues and Model Validation|
|14.30||Hands-on: Estimation of Parameters in Physics-based Models|
|16.00||Hands-On: Genetic/Search Algorithms|
|09.00||Grey-Box Models – The System Structure and Essential Elements|
|10.00||Parameter Estimation for Grey-Box Models|
|11.15||Hands-On: Grey-Box Model Identification|
|13.15||Non-linear system Modeling through Multiple Linear Models|
|14.15||Hands-On: Multiple-Model Approach|
|15.30||Neural Networks as Universal Approximators: Static and Dynamic Models|
|16.30||Hands-On: Neural-Network Model Identification Procedure|