Modelling, Identification and Parameter Estimation Methods
26th - 27th November 2013
This training course is open to all but employees of ACTC member companies, are entitled to two places free of charge. There is a fee for employees of companies that are not ACTC members. Special "early bird" rates are avaliable for registrations received 4 weeks before the course start date (see online registration form). We can also offer a 10% discount for booking 4 or more places (whether it is on one course or spread over 2 - 4 courses), Note that all courses offered can also be provided at your company premises through special arrangements, please contact us for more information..
Registration is required. Please ensure the payment details section of the form is completed.
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.
This course will be held in Scottish Engineering, 105 West George Street, Glasgow G2 1QL. Tel: 0141 221 3181
Glasgow City Centre offers a wide range of accommodation, you can find our recommendations here.
|Day 1 : Modelling and System Identification|
|9.00||Modelling and System Identification for Linear Systems|
|11.15||Hands-On Session: System ID with Least Squares Algorithm|
|12.00||Hands-On Session: System ID with a Kalman Filter to Estimate an Offset|
|13.30||System Identification Implementation Issues and Model Validation|
|14.30||Hands-On Session: Estimation of Parameters in Physics-based Models|
|Day 2 : Parameter Estimation using Grey-Box and Multiple Linear Models|
|09.00||Grey-Box Models – The System Structure and Essential Elements|
|10.00||Parameter Estimation for Grey-Box Models|
|11.15||Hands-On Session: Grey-Box Model Identification|
|13.15||Non-linear system Modeling through Multiple Linear Models|
|14.15||Hands-On Session: Multiple-Model Approach|
|15.30||Neural Networks as Universal Approximators : Static and Dynamic Models|
|16.30||Hands-On Session: Neural-Network Model Identification Procedure|