Optimisation and System Identification
University of Strathclyde, Glasgow, UK, 7th - 9th May 2008

This meeting has since been held. (Register your interest for the next course here.)


This three-day course introduces two closely related topics of Optimisation and System Identification.

The Optimisation is extremely important subject used across all the Engineering fields. For control engineering applications it is widely used for optimal control and parameter identification/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.

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.

Day 1: Optimisation
08.45 Registration
09.00 Static optimisation methods: Unconstrained Optimisation
09.45 Static optimisation methods: Constrained Optimisation
10.45 Tea/Coffee
11.00 Hands-on: Static Optimisation Methods
12.30 Lunch
13.30 Dynamic Optimisation Methods
14.30 Hands-On: Dynamic Optimisation Methods
15.30 Tea/Coffee
15.50 Genetic and Search Algorithms
16.30 Hands-On: Genetic/Search algorithms
17.00 Close
Day 2: Linear Systems Identification
09.00 System Identification for Linear Systems
10.45 Tea/Coffee
11.00 Hands-on: System ID with Least Squares Algorithm
11.45 Hands-on: System ID with a Kalman Filter to Estimate an Offset
12.30 Lunch
13.15 System Identification Implementation Issues and Model Validation
14.30 Hands-On: Estimation of Parameters in Physics-based Models
15.45 Tea/Coffee
16.00 Self-Tuning Control
17.00 Close
Day 3: Non-Linear System Identification
09.00 Grey-Box models - Physical System Structure with Black-Box Elements
10.00 Parameter Estimation for Grey-Box Models
11.00 Tea/Coffee
11.15 Hands-on: Grey-Box Model Identification
12.30 Lunch
13.15 Non-linear System Modeling through Multiple Linear Models
14.15 Hands-On: Multiple-Model Approach
15.15 Tea/Coffee
15.30 Neural Networks as Universal Approximators: Static and Dynamic Models
16.30 Demonstration of the Neural-Network Model Identification Procedure
17.00 Close