Lesson schedule and links for a.a. 2022-2023

Monday 11 a.m.-2 p.m. (Room A3 Via Ariosto 25)


Thursday 11 a.m.-1 p.m. (Room A7 Via Ariosto 25)

All students are required to enroll in the GOOGLE CLASSROOM of Filtering and Optimal Control for any kind of info, recorded lessons and any other item related to the course


classroom code: Kadlymc


invitation link: https://classroom.google.com/c/NjI1OTgyNjQ4NDJa?cjc=kadlymc

(6 ECTS - active since a.a. 2020-2021)


Basics on probability theory: probability spaces, random vectors, distributions and probability densities, expectations and conditional expectations. Hilbert space of random vectors. Projection theorem.
Optimality criteria: centering, consistency, efficiency. Rao-Cramer lower-bound for error covariance.
Optimal estimation: weighted least square estimates, maximum likelihood estimates and Bayesian estimates  with minimum error variance.
Recursive optimal estimators: Kalman filter. Steady state Kalman filter. Kalman filter with correlated state and measurement noise. Recursive weighted least squares, recursive parameter identification

Textbooks and Notes

I. Notes on Filtering and Optimal Control (module II - 6 ECTS - active since a.a. 2020-2021)


1^ Module (probability spaces and random variables)
2^ Module (random vectors, orthogonality of random variable, types of convergence)
3^ Module (conditional probabilities and expectations, Bayes formulas)
4^ Module (Introduction to estimation theory, deterministic and stochastic estimates, stochastic estimates with minimum error variance)
5^ Module (Comparison criteria for optimal estimates: centering, efficiency, consistency; Cramer-Rao lower bound; weighted least squares and maximum likelihood estimators, optimal estimation of parameters and random variables)
6^ Module (Kalman filter; steady state Kalman filter; extended Kalman filter)

II. Notes on System Identification and Optimal Control (module II - 6 ECTS - a.a. 2019-2020)


1^ Module (probability spaces and random variables)
2^ Module (random vectors, orthogonality of random variable, types of convergence)
3^ Module (conditional probabilities and expectations, Bayes formulas)
4^ Module (Introduction to estimation theory, deterministic and stochastic estimates, stochastic estimates with minimum error variance)
5^ Module (Comparison criteria for optimal estimates: centering, efficiency, consistency; Cramer-Rao lower bound; weighted least squares and maximum likelihood estimators, optimal estimation of parameters and random variables)
6^ Module (Recursive estimators: Kalman filter; steady state Kalman filter; extended Kalman filter)
7^ Module (Kalman filter with correlated state noise, recursive weighted least squares, recursive parameter identification)

III. Notes on System Identification and Optimal Control (module II - 8 ECTS - a.a. 2018-2019)


1^ Module (probability spaces and random variables)
2^ Module (random vectors, orthogonality of random variable, types of convergence)
3^ Module (conditional probabilities and expectations, Bayes formulas)
4^ Module (Introduction to estimation theory, deterministic and stochastic estimates, stochastic estimates with minimum error variance)
5^ Module (Comparison criteria for optimal estimates: centering, efficiency, consistency; Cramer-Rao lower bound; weighted least squares and maximum likelihood estimators, optimal estimation of parameters and random variables)
6^ Module (Kalman filter; steady state Kalman filter; extended Kalman filter)
7^ Module (System Identification: identifiability and persistency of excitation)


IV. Notes on System Identification and Optimal Control (module II - 9 ECTS - before a.a. 2018-2019)


Notes (Errata corrige)

Supplementary Textbooks (optional)


C. Bruni, C. Ferrone : Metodi di stima e filtraggio e l'identificazione dei sistemi, Aracne ed. 2008.
M. Dalla Mora, A. Germani, C. Manes : Introduzione alla teoria dell'identificazione dei sistemi, EuRoma ed. 1997.

Video Lessons


The video lessons of Filtering and Optimal Control (module II - 6 ECTS) are available on Google Classroom.

Exam Info

The exam consists of an oral test. The oral test consists of two or three technical questions on the topics covered during the course (see the syllabus).

Certificates