The model-based approach to control systems design calls for analytical description of the process to be controlled. Usually, the model is worked out by resorting to appropriate correlations or physical laws capturing the relationships among the variables of interest. However, it is a common experience that the obtained model suffers from uncertainty.
Identification methods enable to estimate unknown parameters and/or unknown signals, or the complete process model, by squeezing the information hidden in experimental data drawn from measurements of the process variables.
A main rationale to evaluate the quality of an estimated model is to assess its predictive capability. This is why prediction theory is an important preliminary step. Among the topics covered by the course, Kalman filter theory, a major engineering achievement, will be thoroughly studied as a tool for the identification of the state of a process from input-output measurements.
The course consists of about 60 hours of theoretical lectures and 40 hours of tutorial classes.
Data analysis and model identification are nowadays subjects of several software tools, used both for academic and industrial purposes. The laboratory classes aim to help students familiarizing with these tools, and in particular with the Matlab Identification Toolbox.