AIMS AND CONTENT

LEARNING OUTCOMES

The goal of the course is to provide methodologies and tools for designing systems’ models to be used for control, estimation, diagnosis, prediction, etc. Different identification methods are considered, both in a “black box” context (where the structure of the system is unknown), as well as in a “grey box” (uncertainty on parameters) one. Methods are provided for choosing the complexity of the models, for determining the values of their parameters, and to validate them. Moreover, state estimation problems are addressed and their connections with control and identification are considered.

SYLLABUS/CONTENT

Different models for dynamic systems and their applications.
- Parametric and non-parametric models
- Identification techniques for linear models.
- Nonlinear models. Examples and identification methods.
- Validation procedures.
- Introduction to state estimation.
- State estimation in the presence of disturbances.
- Kalman filter and its extension to the nonlinear case.
- Techniques for parameter identification of linear systems in the presence of disturbances.

RECOMMENDED READING/BIBLIOGRAPHY

L. Ljung, “System Identification: Theory for the User”, Prentice Hall
Y. Bar-Shalom, X. R. Li, T. Kirubarajan, “Estimation with Applications to Tracking and Navigation”, John Wiley & Sons

Further readings will be given by lecturer

TEACHERS AND EXAM BOARD

Exam Board

MARCO BAGLIETTO (President)

RENATO UGO RAFFAELE ZACCARIA (President)

MICHELE AICARDI

GIUSEPPE CASALINO

EXAMS

EXAM DESCRIPTION

Oral

ASSESSMENT METHODS

After completing this course the students will be able to:
- design models for dynamic systems from input/output data
- implement algorithms for state estimation

Exam schedule