Modelling, Identification and Estimation

19th & 20th Sept 2017

ISC is pleased to announce that registration is now open for this course. We are offering an "Early Bird Discount" if registration is received 2 weeks before the course start date (see online registration form). Employees of ACTC member companies are entitled to two places free of charge. 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.

Additional Information
Event Venue

Glasgow City Centre


Glasgow City Centre offers a wide range of accommodation, you can find our recommendations here.

Day 1: Introduction to System Identification
08.45 Registration
09.00 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
12.45 LUNCH
13.30 System Identification Implementation Issues and Model Validation
14.30 Hands-On Session: Estimation of Parameters in Physics-based Models
16.00 Self-Tuning Control
17.00 CLOSE
Day 2: System Identification 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
12.30 LUNCH
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
17.00 CLOSE