System Identification
Syllabus, Master's level, 1RT880
This course has been discontinued.
- Code
- 1RT880
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Technology A1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 16 March 2010
- Responsible department
- Department of Information Technology
Entry requirements
120 credits. Signals and systems, Automatic control I, Automatic control II.
Learning outcomes
Students who pass the course should be able to
- describe the different phases that constitute the process of building models, from identification experiment to model validation
- account for and apply the stochastic concepts used in analysis of system identification methods
- explain why different system identification methods and model structures are necessary in engineering practice
- describe and motivate basic properties of identification methods like the least squares method, the prediction error method, the instrumental variable method, as well as to solve simple problems that illustrate these properties
- explain the advantages and challenges when identifying feedback systems in closed loop
- describe the principles behind recursive identification and its fields of application
- explain the usefulness of realisation theory in the context of system identification, and how it is employed in subspace identification techniques
- show hands-on experience with analysing actual data, have working knowledge of the available tools, and to reason how to choose identification methods and model structures for real-life problems.
Content
The system identification problem - from data to model, recursive and batch. Model structures and input signals. The least squares, prediction error and the instrumental variable approaches. The stochastic setting. Model validation and practical aspects. Identification of feedback systems. Recursive identification schemes. State space representations. Deterministic realisation theory. Subspace identification for multivariable (MIMO) systems. Stochastic realisation and subspace identification.
Laboratory work.
Project work: The theoretical study will be complemented with project works on realistic case studies. Emphasis will be given to hands-on practice, implementing algorithms, data processing and critical evaluation of results, culminating in a written report.
Instruction
Lectures, laboratory work and project work.
Assessment
Written examination (7 credits) and project work (3 credits).