MODEL IDENTIFICATION AND DATA ANALYSIS
cod. 1010560

Academic year 2022/23
1° year of course - Second semester
Professor
CONSOLINI Luca
Academic discipline
Automatica (ING-INF/04)
Field
Attività formative affini o integrative
Type of training activity
Related/supplementary
48 hours
of face-to-face activities
6 credits
hub: PARMA
course unit
in ENGLISH

Learning objectives

Acquired knowledge: the student will acquire methods and basic knowledge for identifying mathematical models of systems and plants. The student will also acquire the basic skills for designing adaptive systems.

Applying knowledge: the student will apply the acquired knowledge to identify industrial processes and to design industrial adaptive controls.

Independence of thinking: the student will be able to understand and critically evaluate existing mathematical plant models and adaptive controllers.

Communication skills: the student will acquire the specific language for modelling and adaptive control.Learning autonomy: the student will be able to learn on his/her own new specific related topics.

Prerequisites

Fundamentals of automatic control.
Linear systems theory.

Course unit content

1-Review on signals and systems (4 hrs)
2-Nonparametric Identification (8 hrs)
3-Parametric identification (20 hrs)
4-Subspace-based methods (16 hrs)

Full programme

1-Review on signals and systems (4 hrs)

2-Nonparametric Identification (8 hrs)
Impulse and Step Response
Correlation Methods

3- Parametric identification (20 hrs)
Dynamic models, ARMA, ARMAX.
Estimation theory, least squares, prediction error methods.
Forecasting methods.

4- Subspace-based methods (16 hours)
SVD decomposition.
Elements of realization theory.
MOESP method.

Bibliography

For consultation:

1) Lennart Ljung: System Identification (2nd Edition), Prentice Hall.

2) P. A. Ioannou and J. Sun, Robust Adaptive Control, Courier Dover Publications, 2012. Freely downloadable from http://www-bcf.usc.edu/∼ioannou/RobustAdaptiveBook95pdf/Robust Adaptive Control.pdf

Teaching methods

The course will have both frontal lectures and laboratory activities at the computer. Reference material and solved exercises will be placed on Elly platform.

Assessment methods and criteria

The written exam will be evaluated up to 30 points. The project will be evaluated up to 3 points. If the total grade is 32 or 33, 30 cum laude will be awarded. If it is 30 or 31, 30 will be awarded.

Other information