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SYSTEM IDENTIFICATION

CODE 80186
ACADEMIC YEAR 2022/2023
CREDITS
  • 4 cfu during the 2nd year of 10635 ROBOTICS ENGINEERING (LM-32) - GENOVA
  • 6 cfu during the 1st year of 11160 COMPUTER ENGINEERING (LM-32) - GENOVA
  • 5 cfu during the 2nd year of 8732 INGEGNERIA ELETTRONICA (LM-29) - GENOVA
  • 5 cfu during the 1st year of 10635 ROBOTICS ENGINEERING (LM-32) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/04
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    System identification consists of managing mathematical methods for modeling dynamic systems starting from measured data. Input-output models will be treated, as well as methods based on a state-space representation. State estimation methods will be presented.

    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.

    AIMS AND LEARNING OUTCOMES

    Students will learn how to choose an appropriate model for a system starting from the available input/output data. They also will learn how to set a suitable complexity for the model and how to optimize the involved parameters using data.

    Moreover, ability will be given to deal with state estimation methods both in a linear as well as in a nonlinear context.

     

     

     

    PREREQUISITES

    Fundamentals of Linear System Theory and Classical Control Theory.

    TEACHING METHODS

    The course consists of classroom lectures

    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

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Oral exam with discussion of system identification and state estimation methods and possible applications.

    Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

    ASSESSMENT METHODS

    The students will be evaluated on the basis of their capability to describe system identification and state estimation algorithms, to choose suitable models depending on the application context and to appropriately use a data set to optimize the complexity and the parameters of models.

    Exam schedule

    Date Time Location Type Notes