System identification
Summary
Identification of discrete-time linear models using experimental data is studied. The correlation method and spectral analysis are used to identify nonparametric models and the subspace and prediction error methods to estimate the plant and noise model parameters. Hands-on labs are included.
Content
Models (classifications, representations). Excitation signals (impulse, step, random, pseudo random). Least Squares algorithm (linear regression, analysis in stochastic case, bias-variance tradeoff). Time-domain nonparametric identification methods (impulse response by the correlation approach). Frequency-domain nonparametric identification methods based on the Fourier and spectral analysis. Parametric identification by linear regression (least squares method, instrumental variables method, recursive algorithms). Subspace identification methods. Prediction error methods (ARX, ARMAX, OE and BJ structures). Plant model identification in closed-loop operation. Introduction to nonlinear model identification.
Keywords
System identification, spectral analysis, correlation approach, prediction error method
Learning Prerequisites
Recommended courses
Dynamic systems, Control systems
Important concepts to start the course
- Represent a physical process as a system with its input, outputs and disturbances
- Analyze a linear dynamical system (both time and frequency response)
- Represent a linear system by a transfer function (discrete- and continuous-time)
Learning Outcomes
By the end of the course, the student must be able to:
- Identify a dynamic system using experimental data, A6
- Construct and analyze a discrete-time model for a dynamic system, A5
Transversal skills
- Write a scientific or technical report.
- Plan and carry out activities in a way which makes optimal use of available time and other resources.
- Set objectives and design an action plan to reach those objectives.
Teaching methods
Ex-cathedra course with hands-on labs and project
Expected student activities
Hands-on laboratory for groups of two students, preparing technical reports.
Assessment methods
Written test (70%) and lab reports (30%).
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | No |
Resources
Notes/Handbook
Course-notes (in English): System Identification
Slides available (pdf) in English
Moodle Link
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: System identification
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks