Gianfranco PARLANGELI

Gianfranco PARLANGELI

Ricercatore Universitario

Settore Scientifico Disciplinare ING-INF/04: AUTOMATICA.

Dipartimento di Ingegneria dell'Innovazione

Centro Ecotekne Pal. O - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio, Piano terra

Telefono +39 0832 29 7306

Area di competenza:

Settore disciplinare ING-INF/04 denominato 'Automatica'

Orario di ricevimento

 

Lunedì 10.30-12.30

Venerdì 10.30-12.30

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Curriculum Vitae

Gianfranco Parlangeli received the Laurea degree (with honours) in electrical engineering from the University of Pisa, Pisa, Italy, in 1999 and the Ph.D. degree in information engineering from the University of Lecce, Lecce, Italy, in 2005. He is currently an Assistant Professor at the Department of Innovation Engineering, University of Salento, Lecce, Italy. He is member of scientific committee of the Interuniversity Center of Integrated Systems for the Marine Environment (ISME). His research interests include multi-agent systems, fault tolerant control, variable structure control systems and marine robotics. He has published over 50 papers in the field and has contributed to several national and international projects in the area of autonomous robotics and industrial automation.

Estimation and Data Analysis with Applications

 CdlM Computer Engineering - 9 CFU

Programma sintetico:

Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation. The Bayesian approach to the estimation problem. Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter. Applications of Kalman Filter.

Set membership estimation: introduction, fundamental results and theorems. Set membership estimation: some applications. Robust estimation: introduction, fundamental definitions, estimator classes and performances. Data driven by unknown external entities: vulnerability analysis, resilient estimator design. Applications of the previous issues and results to various fields. Data analysis: mathematical tools, foundations. Elements of clustering and classification. The electric power system state estimation. Overview of Electric Power System State Estimation techniques.

 

The following textbooks are reccomended together with papers provided by the instructor:

Ljung, Lennart. "System Identification: Theory for the user" Englewood Cliffs, 1987.

Anderson, Brian DO, and John B. Moore. "Optimal Filtering" (1979). 

Milanese, M., Norton, J., Piet-Lahanier, H., & Walter, É. (Eds.). (2013). Bounding approaches to system identification. Springer Science & Business Media.

 

Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and algorithms”, Cambridge University Press, 2014.

 

The exam is an oral discussion (including possibly one written exercise) and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm.

Didattica

A.A. 2020/2021

ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2020/2021

For matriculated on 2020/2021

Course year 1

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2020/2021

For matriculated on 2019/2020

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

FONDAMENTI DI AUTOMATICA

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Tipo corso di studio Laurea

Lingua ITALIANO

Crediti 7.0

Ripartizione oraria Ore Attività frontale: 63.0

Anno accademico di erogazione 2020/2021

Per immatricolati nel 2018/2019

Anno di corso 3

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

ROBOTICS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2020/2021

For matriculated on 2019/2020

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2019/2020

ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2019/2020

For matriculated on 2019/2020

Course year 1

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2019/2020

For matriculated on 2018/2019

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2018/2019

ADVANCED CONTROL TECHNIQUES

Degree course MATEMATICA

Course type Laurea Magistrale

Language INGLESE

Credits 12.0

Teaching hours Ore Attività frontale: 84.0

Year taught 2018/2019

For matriculated on 2018/2019

Course year 1

Structure DIPARTIMENTO DI MATEMATICA E FISICA "ENNIO DE GIORGI"

Subject matter APPLICATIVO

Location Lecce

ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 12.0

Teaching hours Ore Attività frontale: 108.0

Year taught 2018/2019

For matriculated on 2018/2019

Course year 1

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2018/2019

For matriculated on 2017/2018

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2017/2018

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2017/2018

For matriculated on 2016/2017

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2016/2017

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2016/2017

Per immatricolati nel 2015/2016

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

A.A. 2015/2016

MULTIVARIABLE ESTIMATION AND CONTROL

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2015/2016

Per immatricolati nel 2014/2015

Anno di corso 2

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

Torna all'elenco
ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2020/2021

Year taught 2020/2021

Course year 1

Semestre Secondo Semestre (dal 01/03/2021 al 11/06/2021)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, linear algebra, systems and signals, systems theory.

This course offers a broad overview of fundamental and emerging topics in the area of control and systems theory. Applications are illustrated in the fields of robotics, multi-agent systems and cyber-physical systems. It is aimed at providing principles and tools to state and solve optimal control problems eventually seeking distributed control architectures in several technological systems, and the solution is sought both analitically through direct computation and also numerically with the aid of a suitable software (Mathworks Matlab is used in the course).

Learning Outcomes; after the course the student should be able to:

(Conoscenze e comprensione) Describe and explain the main peculiarities (both advantages and disadvantages) of the classical and modern control theory considered in the course.

(Capacità di applicare conoscenze e comprensione)+ (Abilità comunicative) + (Autonomia di giudizio) Be aware of, describe and explain practical problems of controlling complex systems, and how to overcome these drawbacks using modern approaches.

(Capacità di applicare conoscenze e comprensione)+ (Capacità di apprendimento) For a given practical problem at hand, the student should be able to state a control problem in a natural mathematical setting, eventually seeking distributed architectures, based on the problem assumptions.

(Capacità di applicare conoscenze e comprensione) +(Abilità comunicative) + (Autonomia di giudizio) Starting from a theoretical formulation of a problem, the student should be able to build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

(Abilità comunicative)+(Capacità di apprendimento) Willing students may develop a project on an application of interest where to apply the methodologies developed along the course. 

Lezioni frontali svolte in aula dal docente tramite l'ausilio di gesso e lavagna. Nel corso delle lezioni saranno occasionalmente illustrati e discussi  software commerciali.

The exam is a written exam and an oral discussion, and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek an analytical solution. Additionally, willing students may have a seminar or a project on an application of interest where the methodologies of the course are applied.

Introduction. Mathematical background and connections with other courses (2 hours). Background on Systems theory and linear algebra. Jordan form of a matrix. Linear systems, unforced response and forced response. Exponential and raise to a power of a square matrix. Stability of a linear system and Lyapunov Equation. (10 hours). Linear systems controllability and observability. Eigenvalues placement through state feedback: Rosenbrock theorem. Kalman decomposition of a linear system (7 hours). Introduction to optimal control. Extremum seeking techniques. Functionals. Normed vector spaces. Weak and strong extremum. Differentiable functionals and first variation. (7 hours) Calculus of variations, Euler equation: derivation, comments, examples (10 hours). The Bellman's optimal principle: statement, examples. Cost to go. Costate variables. The optimal control problem solved using the Bellman approach for continuous time systems: HJB equation. Derivation. Examples. (10 hours). The optimal control problem in the presence of saturation: the Pontryagin's maximum principle (6 hours). The linear quadratic optimal control problem. Statement and solution using the variational approach. (6 hours). Discussion on the issues of extending the horizon to infinity. Main theorems. Riccati and Lyapunov equations. Nonsingular solutions of the Riccati Equation. (8 hours). Multi agent systems: an introduction. Examples, main definitions. Centralized architectures vs decentralized ones. Supervisory control, distributed control. (4 hours).Some notions of Graph theory. Dynamical systems over graphs. (7 hours). The importance of consensus in various emerging fields. Consensus protocols. Consensus networks. Analysis of consensus within a multi-agent dynamical system. (6 hours). Consensus problems for directed graphs. Leader-follower multi-agent systems. Symmetries and equitable partitions (3 hours). Directed weighted graphs: a model for consensus networks and cyber-physical systems. Analysis, properties. Differences between directed weighted graphs and undirected weighted graphs. Examples (7 hours). Misbehaving nodes and intruders in a collaborative network . System zeros and output-nulling inputs. Rosenbrock's system matrix. Unobservable zeros and transmission zeros. (5 hours).

[1] Antsaklis, P. J., & Michel, A. N. (2006). Linear systems. Springer Science & Business Media.

[2] Anderson, Brian DO, and John B. Moore, Optimal control: linear quadratic methods, Courier Corporation, 2007.

[3] Bullo, F. Lectures on Network Systems, with contributions by J. Cortes, F. Dorfler and S. Martinez, Kindle Direct Publishing, 2018.

ADVANCED CONTROL TECHNIQUES (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2019/2020

Year taught 2020/2021

Course year 2

Semestre Secondo Semestre (dal 01/03/2021 al 11/06/2021)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, probability theory, linear algebra.

This course offers a broad overview of fundamental and emerging topics in the area of estimation theory and data analysis; furthermore, a set of applications are illustrated in the fields of robotics, multi-agent and cyber-physical systems, and social systems. It is aimed at providing principles and tools to state and solve estimation problems in technological systems, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab).

Learning Outcomes. After the course the student should be able to:

 

(Knowledge and understanding)

Describe and explain the main peculiarities (both advantages and disadvantages) of each mathematical framework for the estimation problems considered in the course.

 

(Applying knowledge and understanding) + (Communication) + (Making judgements)

Be aware of, describe and explain practical problems of bad data gathering and robustness issues in the framework of estimation theory.

 

(Applying knowledge and understanding) + (Learning skills)

For a given practical problem at hand, be able to state an estimation problem in a natural mathematical setting, either stochastic or deterministic, based on the problem assumptions.

 

(Applying knowledge and understanding) + (Communication) + (Making judgements)

Build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

Frontal lessons and lectures.

Oral exam and development of a project.

The objective of the exam is to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm.

Introduction. Mathematical background and connections with other courses.

Set membership estimation: introduction, fundamental results and theorems. Set membership estimation: some applications.

Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation. The Bayesian approach to the estimation problem. Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter. Applications of Kalman Filter. Smoothing Algorithms. Robust estimation: introduction, fundamental definitions, estimator classes and performances.  

Applications of the previous issues and results to various fields.

Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan “Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software”, 2001 John Wiley & Sons, Inc.

 

D. Simon, “Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches”, John Wiley & Sons, 2006

 

Anderson, Brian D.O., and John B. Moore. “Optimal Filtering”, 1979.

 

L. Ljung, “System Identification: Theory for the User”, Prentice Hall PTR, Upper Saddle River, NJ, 1999.

 

Rousseeuw PJ, Leroy AM. “Robust Regression and Outlier Detection”. John Wiley & Sons: Hoboken, NJ, USA, 2003.

 

Huber PJ, Ronchetti EM. “Robust Statistics” - Second Edition. Wiley: New York, 2009.

 

Milanese, M., Norton, J., Piet-Lahanier, H., Walter, É. (Eds.). (2013). “Bounding approaches to system identification” Springer Science & Business Media.

 

Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and algorithms”, Cambridge University Press, 2014.

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
FONDAMENTI DI AUTOMATICA

Corso di laurea INGEGNERIA DELL'INFORMAZIONE

Settore Scientifico Disciplinare ING-INF/04

Tipo corso di studio Laurea

Crediti 7.0

Ripartizione oraria Ore Attività frontale: 63.0

Per immatricolati nel 2018/2019

Anno accademico di erogazione 2020/2021

Anno di corso 3

Semestre Primo Semestre (dal 29/09/2020 al 13/01/2021)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Sede Lecce

Segnali e sistemi

Il corso mira a fornire i concetti e gli strumenti metodologici di base per l'analisi e la sintesi di sistemi di controllo a tempo continuo, lineari, tempo invarianti a singolo ingresso e singola uscita.

Conoscenza e comprensione: Fornire adeguate conoscenze al fine di far comprendere il ruolo dei sistemi di controllo per impianti SISO (single input - single output) lineari tempo invarianti. In particolare i risultati di apprendimento attesi sono relativi alla comprensione dei meccanismi di controllo in catena aperta ed in ciclo chiuso. Centrali sono i concetti di stabilità di sistemi dinamici SISO, robustezza ad incertezze di modello e disturbi esogeni. Capacità di applicare conoscenza e comprensione: La capacità di applicare le conoscenze acquisite è relativa alla comprensione e definizione di specifiche di controllo (sia in frequenza che nel dominio del tempo) e, quindi, alla capacità di sintesi di sistemi di controllo per impianti SISO lineari tempo invarianti. I risultati saranno verificati in sede di esame, ma anche valutando la partecipazione degli studenti alle attività didattiche frontali e seminariali. Autonomia di Giudizio, abilità comunicative: L'autonomia di giudizio si dovrà manifestare dimostrando padronanza dei concetti e dei metodi descritti nel corso per la sintesi di sistemi di controllo generalizzando quanto illustrato nel corso ad impianti SISO lineari tempo invarianti arbitrari. Capacità di apprendimento: La capacità di apprendimento sarà valutata (qualitativamente) durante i ricevimenti e le esercitazioni che saranno improntate alla massima partecipazione attiva possibile. La capacità di apprendimento finale sarà valutata globalmente e quantitativamente in sede di esame.

Attività didattica frontale, esercitazioni ed eventuali attività seminariali.

L'esame finale si compone di una prova scritta ed una discussione orale da svolgersi nello stesso periodo degli esami. La prova scritta consiste nella risoluzione di esercizi di analisi e sintesi di sistemi di controllo (tipicamente lineari tempo invarianti) ed ha come obiettivo primario quello di verificare la conoscenza e la comprensione della materia. Nel risolvere la prova scritta i candidati sono chiamati a dimostrare la capacità di applicare le loro conoscenze e competenze su casi concreti identificando le informazioni pertinenti ed utilizzando correttamente i dati forniti per risolvere i problemi posti (criteri di Dublino).

• Introduzione al corso ed ai concetti fondamentali. Lo schema del controllo ad azione diretta ed in retroazione: considerazioni generali. Introduzione al concetto di robustezza ai disturbi e alle variazioni parametriche degli impianti. Richiami sulle equazioni differenziali e loro classificazione. Richiami sul concetto di equilibrio e di stabilita' per equazioni differenziali autonome. Stabilita' e convergenza nel caso di equazioni lineari e nonlineari. • Modelli per lo studio dei sistemi di controllo. Richiami sulla modellistica ingresso/uscita e nello spazio degli stati. Richiami sulle trasformate di Laplace e loro uso per la soluzione di equazioni LTI. La funzione di trasferimento e la trasformata della risposta libera. Introduzione all'algebra dei blocchi ed analisi di sistemi interconnessi. Riduzione di schemi a blocchi. Esame preliminare del sistema in retroazione elementare. Riduzione degli schemi a blocchi per sistemi interconnessi. Introduzione ai sistemi del secondo ordine. Introduzione alla formulazione standard in termini di pulsazione naturale e coefficiente di smorzamento. Analisi dimensionale. • I sistemi elementari del primo e secondo ordine nel dominio del tempo. Risposte indiciali ed impulsive dei sistemi elementari del primo e secondo ordine. Introduzione al concetto di poli dominanti. Introduzione all'analisi del ruolo degli zeri. • Analisi armonica e diagrammi polari. Analisi armonica. La funzione di risposta armonica, i diagrammi di Bode ed i diagrammi polari. Regole di tracciamento ed analisi dei sistemi elementari del I e del II ordine in frequenza. Analisi del ruolo degli zeri. Introduzione ai sistemi a fase non minima. Effetto di ritardi finiti. • La stabilita' dei sistemi in retroazione. Introduzione al concetto ed allo studio della stabilita' in retroazione. Il criterio di Nyquist. Il concetto della robustezza. I criteri del margine di fase e di guadagno. Il criterio della pendenza o di Bode. Generalizzazione del criterio del margine di fase per sistemi instabili. Il criterio di Routh-Hurwitz. • Le specifiche dei sistemi di controllo e la sintesi dei regolatori. Le specifiche dei sistemi di controllo nel dominio del tempo e della frequenza. Prestazioni statiche e dinamiche. Reiezione dei disturbi e sensitività a variazioni parametriche. Cenno al ruolo del trasduttore. Il luogo delle radici. Le reti standard: reti ad anticipo di fase, reti a ritardo di fase, reti PID. La sintesi in frequenza per sistemi a fase non minima e per impianti instabili. Limitazioni alla prestazioni ottenibili per impianti a fase non minima o instabili. • Implementazione a tempo discreto di regolatori sintetizzati a tempo continuo. (6 ore) Richiami sul campionamento e sui sistemi a tempo discreto. Richiami sulla trasformata z. Discretizzazione di funzioni di trasferimento in frequenza: i metodi di Eulero in avanti, Eulero all'indietro, Tustin. Pseudo-codice di regolatori elementari a tempo discreto. • Sintesi diretta di controllori a tempo discreto. Equazione diofantea.

Titolo: Sistemi di controllo Autore: Alberto Isidori Editore: Roma : Siderea, 1992

FONDAMENTI DI AUTOMATICA (ING-INF/04)
ROBOTICS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2019/2020

Year taught 2020/2021

Course year 2

Semestre Primo Semestre (dal 22/09/2020 al 18/12/2020)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, mechanics, control theory and linear algebra

This course offers a broad overview of fundamental topics in the area of robotics, mobile robotics and multi-robotic systems. It is aimed at providing principles and tools to state and solve the design problems for industrial robots and mobile devices, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab is used in the course).

Ability to apply knowledge and understanding) Describe and explain the main peculiarities (both advantages and disadvantages) of each facet of the design of a robotic, mobile robotic and multi-robotic systems. (Ability to apply knowledge and understanding) + (Communication skills) + (Autonomy of judgment) Be aware, describe and explain the practical problems of controlling complex systems and how to overcome these drawbacks using modern approaches. (Ability to apply knowledge and understanding) + (Learning ability) + (Autonomy of judgment) Starting from a practical problem, the student must be able to formalize an adequate theoretical formulation, and also should be able to build a framework of simulation to find a computer solution of the mathematical problem with the use of a suitable software. (Communication skills) + (Learning skills) Students can develop a project on an application of interest in which to apply the methodologies developed along the course.

Lezioni frontali svolte in aula dal docente tramite l'ausilio di gesso e lavagna. Nel corso delle lezioni saranno occasionalmente illustrati e discussi software commerciali.

The exam is an oral discussion (including possibly one written exercise) and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm.

Introduction to Robotics. Robot Mechanical Structures. Robot Manipulators, Mobile Robots, Industrial robotics. Advanced Robotics, Field Robots, Service Robots. Robot Modelling, Planning and Control. Mathematical background and connections with other courses. Kinematics. Euler Angles. Denavit–Hartenberg Convention.Kinematics of Typical Manipulator Structures. The Inverse Kinematics Problem. Differential Kinematics and Statics. Geometric Jacobian. Kinematic Singularities. Analysis of Redundancy.Statics. Kineto-Statics Duality. Trajectory Planning. Joint Space Trajectories. Dynamics. Lagrange Formulation. Newton–Euler Formulation. Dynamic Manipulability Ellipsoid. Motion Control. Force Control. Mobile Robots. Nonholonomic Constraints. Kinematic Model, Dynamic Model. Planning, Motion Control.

Title: Robotics: Modelling, Planning and Control Authors: Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G. Publisher: Springer-Verlag London Copyright Year: 2009

ROBOTICS (ING-INF/04)
ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2019/2020

Year taught 2019/2020

Course year 1

Semestre Secondo Semestre (dal 02/03/2020 al 05/06/2020)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, linear algebra, systems and signals, systems theory.

This course offers a broad overview of fundamental and emerging topics in the area of control and systems theory. Applications are illustrated in the fields of robotics, multi-agent systems and cyber-physical systems. It is aimed at providing principles and tools to state and solve optimal control problems eventually seeking distributed control architectures in several technological systems, and the solution is sought both analitically through direct computation and also numerically with the aid of a suitable software (Mathworks Matlab is used in the course).

Learning Outcomes; after the course the student should be able to:

(Conoscenze e comprensione) Describe and explain the main peculiarities (both advantages and disadvantages) of the classical and modern control theory considered in the course.

(Capacità di applicare conoscenze e comprensione)+ (Abilità comunicative) + (Autonomia di giudizio) Be aware of, describe and explain practical problems of controlling complex systems, and how to overcome these drawbacks using modern approaches.

(Capacità di applicare conoscenze e comprensione)+ (Capacità di apprendimento) For a given practical problem at hand, the student should be able to state a control problem in a natural mathematical setting, eventually seeking distributed architectures, based on the problem assumptions.

(Capacità di applicare conoscenze e comprensione) +(Abilità comunicative) + (Autonomia di giudizio) Starting from a theoretical formulation of a problem, the student should be able to build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

(Abilità comunicative)+(Capacità di apprendimento) Willing students may develop a project on an application of interest where to apply the methodologies developed along the course. 

Lezioni frontali svolte in aula dal docente tramite l'ausilio di gesso e lavagna. Nel corso delle lezioni saranno occasionalmente illustrati e discussi  software commerciali.

The exam is a written exam and an oral discussion, and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek an analytical solution. Additionally, willing students may have a seminar or a project on an application of interest where the methodologies of the course are applied.

Introduction. Mathematical background and connections with other courses (2 hours). Background on Systems theory and linear algebra. Jordan form of a matrix. Linear systems, unforced response and forced response. Exponential and raise to a power of a square matrix. Stability of a linear system and Lyapunov Equation. (10 hours). Linear systems controllability and observability. Eigenvalues placement through state feedback: Rosenbrock theorem. Kalman decomposition of a linear system (7 hours). Introduction to optimal control. Extremum seeking techniques. Functionals. Normed vector spaces. Weak and strong extremum. Differentiable functionals and first variation. (7 hours) Calculus of variations, Euler equation: derivation, comments, examples (10 hours). The Bellman's optimal principle: statement, examples. Cost to go. Costate variables. The optimal control problem solved using the Bellman approach for continuous time systems: HJB equation. Derivation. Examples. (10 hours). The optimal control problem in the presence of saturation: the Pontryagin's maximum principle (6 hours). The linear quadratic optimal control problem. Statement and solution using the variational approach. (6 hours). Discussion on the issues of extending the horizon to infinity. Main theorems. Riccati and Lyapunov equations. Nonsingular solutions of the Riccati Equation. (8 hours). Multi agent systems: an introduction. Examples, main definitions. Centralized architectures vs decentralized ones. Supervisory control, distributed control. (4 hours).Some notions of Graph theory. Dynamical systems over graphs. (7 hours). The importance of consensus in various emerging fields. Consensus protocols. Consensus networks. Analysis of consensus within a multi-agent dynamical system. (6 hours). Consensus problems for directed graphs. Leader-follower multi-agent systems. Symmetries and equitable partitions (3 hours). Directed weighted graphs: a model for consensus networks and cyber-physical systems. Analysis, properties. Differences between directed weighted graphs and undirected weighted graphs. Examples (7 hours). Misbehaving nodes and intruders in a collaborative network . System zeros and output-nulling inputs. Rosenbrock's system matrix. Unobservable zeros and transmission zeros. (5 hours).

[1] Antsaklis, P. J., & Michel, A. N. (2006). Linear systems. Springer Science & Business Media.

[2] Anderson, Brian DO, and John B. Moore, Optimal control: linear quadratic methods, Courier Corporation, 2007.

[3] Bullo, F. Lectures on Network Systems, with contributions by J. Cortes, F. Dorfler and S. Martinez, Kindle Direct Publishing, 2018.

ADVANCED CONTROL TECHNIQUES (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2018/2019

Year taught 2019/2020

Course year 2

Semestre Secondo Semestre (dal 02/03/2020 al 05/06/2020)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, probability theory, linear algebra.

This course offers a broad overview of fundamental and emerging topics in the area of estimation theory and data analysis; furthermore, a set of applications are illustrated in the fields of robotics, multi-agent and cyber-physical systems, social systems and electric networks. It is aimed at providing principles and tools to state and solve estimation problems in technological systems, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab is used in the course).

Learning Outcomes; after the course the student should be able to:

(Conoscenze e comprensione) Describe and explain the main peculiarities (both advantages and disadvantages) of each mathematical framework for the estimation problems considered in the course.

(Capacità di applicare conoscenze e comprensione)+ (Abilità comunicative) + (Autonomia di giudizio) Be aware of, describe and explain practical problems of bad data gathering and robustness issues in the framework of estimation theory.

(Capacità di applicare conoscenze e comprensione)+ (Capacità di apprendimento) For a given practical problem at hand, be able to state an estimation problem in a natural mathematical setting, either stochastic or deterministic, based on the problem assumptions.

(Capacità di applicare conoscenze e comprensione) +(Abilità comunicative) + (Autonomia di giudizio) Build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

(Abilità comunicative)+(Capacità di apprendimento) Willing students may hold a seminar on an application of interest where to apply the methodologies developed along the course. 

Lezioni frontali svolte in aula dal docente tramite l'ausilio di gesso e lavagna. Nel corso delle lezioni saranno occasionalmente illustrati e discussi  software commerciali.

The exam is an oral discussion (including possibly one written exercise) and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm. Additionally, willing students may have a seminar on an application of interest where the methodologies of the course are applied.

Introduction. Mathematical background and connections with other courses (2 hours). Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation (10 hours). The Bayesian approach to the estimation problem (7 hours). Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter (16 hours). Applications of Kalman Filter (6 hours). Set membership estimation: introduction, fundamental results and theorems (8 hours). Set membership estimation: some applications (4 hours). Robust estimation: introduction, fundamental definitions, estimator classes and performances (7 hours). Data driven by unknown external entities: vulnerability analysis, resilient estimator design (6 hours). Applications of the previous issues and results to various fields (3 hours). Data analysis: mathematical tools, foundations. Elements of clustering and classification (7 hours). The electric power system state estimation. Overview of Electric Power System State Estimation techniques. (5 hours).

[1] Ljung, Lennart. "System Identification: Theory for the user" Englewood Cliffs, 1987.

[2] Anderson, Brian DO, and John B. Moore. "Optimal Filtering" (1979).

[3] Milanese, M., Norton, J., Piet-Lahanier, H., & Walter, É. (Eds.). (2013). Bounding approaches to system identification. Springer Science & Business Media.

[4] Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and algorithms”, Cambridge University Press, 2014.

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
ADVANCED CONTROL TECHNIQUES

Degree course MATEMATICA

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 12.0

Teaching hours Ore Attività frontale: 84.0

For matriculated on 2018/2019

Year taught 2018/2019

Course year 1

Semestre Secondo Semestre (dal 25/02/2019 al 31/05/2019)

Language INGLESE

Subject matter APPLICATIVO (022)

Location Lecce

ADVANCED CONTROL TECHNIQUES (ING-INF/04)
ADVANCED CONTROL TECHNIQUES

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 12.0

Teaching hours Ore Attività frontale: 108.0

For matriculated on 2018/2019

Year taught 2018/2019

Course year 1

Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

ADVANCED CONTROL TECHNIQUES (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2017/2018

Year taught 2018/2019

Course year 2

Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, probability theory, linear algebra.

This course offers a broad overview of fundamental and emerging topics in the area of estimation theory and data analysis; furthermore, a set of applications are illustrated in the fields of robotics, multi-agent and cyber-physical systems, social systems and electric networks. It is aimed at providing principles and tools to state and solve estimation problems in technological systems, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab is used in the course).

Learning Outcomes; after the course the student should be able to:

(Conoscenze e comprensione) Describe and explain the main peculiarities (both advantages and disadvantages) of each mathematical framework for the estimation problems considered in the course.

(Capacità di applicare conoscenze e comprensione)+ (Abilità comunicative) + (Autonomia di giudizio) Be aware of, describe and explain practical problems of bad data gathering and robustness issues in the framework of estimation theory.

(Capacità di applicare conoscenze e comprensione)+ (Capacità di apprendimento) For a given practical problem at hand, be able to state an estimation problem in a natural mathematical setting, either stochastic or deterministic, based on the problem assumptions.

(Capacità di applicare conoscenze e comprensione) +(Abilità comunicative) + (Autonomia di giudizio) Build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

(Abilità comunicative)+(Capacità di apprendimento) Willing students may hold a seminar on an application of interest where to apply the methodologies developed along the course. 

Lezioni frontali svolte in aula dal docente tramite l'ausilio di gesso e lavagna. Nel corso delle lezioni saranno occasionalmente illustrati e discussi  software commerciali.

The exam is an oral discussion (including possibly one written exercise) and it is aimed to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm. Additionally, willing students may have a seminar on an application of interest where the methodologies of the course are applied.

Introduction. Mathematical background and connections with other courses (2 hours). Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation (10 hours). The Bayesian approach to the estimation problem (7 hours). Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter (16 hours). Applications of Kalman Filter (6 hours). Set membership estimation: introduction, fundamental results and theorems (8 hours). Set membership estimation: some applications (4 hours). Robust estimation: introduction, fundamental definitions, estimator classes and performances (7 hours). Data driven by unknown external entities: vulnerability analysis, resilient estimator design (6 hours). Applications of the previous issues and results to various fields (3 hours). Data analysis: mathematical tools, foundations. Elements of clustering and classification (7 hours). The electric power system state estimation. Overview of Electric Power System State Estimation techniques. (5 hours).

[1] Ljung, Lennart. "System Identification: Theory for the user" Englewood Cliffs, 1987.

[2] Anderson, Brian DO, and John B. Moore. "Optimal Filtering" (1979).

[3] Milanese, M., Norton, J., Piet-Lahanier, H., & Walter, É. (Eds.). (2013). Bounding approaches to system identification. Springer Science & Business Media.

[4] Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and algorithms”, Cambridge University Press, 2014.

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2016/2017

Year taught 2017/2018

Course year 2

Semestre Secondo Semestre (dal 01/03/2018 al 01/06/2018)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/04

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2015/2016

Anno accademico di erogazione 2016/2017

Anno di corso 2

Semestre Primo Semestre (dal 26/09/2016 al 22/12/2016)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
MULTIVARIABLE ESTIMATION AND CONTROL

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/04

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2014/2015

Anno accademico di erogazione 2015/2016

Anno di corso 2

Semestre Primo Semestre (dal 21/09/2015 al 18/12/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

MULTIVARIABLE ESTIMATION AND CONTROL (ING-INF/04)
MULTIVARIABLE ESTIMATION AND CONTROL

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/04

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Per immatricolati nel 2013/2014

Anno accademico di erogazione 2014/2015

Anno di corso 2

Semestre Secondo Semestre (dal 02/03/2015 al 06/06/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

MULTIVARIABLE ESTIMATION AND CONTROL (ING-INF/04)

Pubblicazioni

 

M. L. Corradini, G. Orlando, A. Manni, G.Parlangeli A Fault Tolerant Control Strategy for Linear Systems Subject to a Class of Faults, 45th IEEE Conference on Decision and Control, December 13-15, 2006, Manchester Grand Hyatt Hotel, San Diego, CA, USA.

 

Indiveri G.,G.ParlangeliOn Planning Smooth Paths for Marine Vehicles,7th IFAC Conference on Manoeuvring and Control of Marine Craft (MCMC'2006) , Lisbon, Portugal, September 20-22, 2006.

 

 

Indiveri G., S.Zano li, G.Parlangeli DC Motor Control Issues for UUVs, IEEE MED 2006 14th Mediterranean, Conference on Control and Automation, June 28 - 30 , 2006 Ancona, Italy.

 

M. L. Corradini, D. Pacella, G.Parlangeli A Fault Tolerant Control Strategy for Linear Systems Subject to a Class of Faults, European Control Conference 2007, Kos, Greece 2-5 July 2007.

 

M. L. Corradini, G. Orlando, V. Orsini, G.Parlangeli Stabilization of a class of nonlinear systems with saturating actuators via sliding mode control  7th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2007), Pretoria, South Africa, 22-24 August, 2007.

G. Indiveri, S.Zanoli, A. Miccoli, G.Parlangeli On a DC motor torque control solution for marine applications ,  7International Symposium on Unmanned Untethered Submersible Technology, 2007.

 

M. L. Corradini, A. Manni, G.Parlangeli Variable Structure Control of Nonlinear Uncertain Sandwich Systems with Nonsmooth Nonlinearities, 46th IEEE Conference on Decision and Control CDC2007  G.Parlangeli, D. Pacella, M. L. Corradini  Fault Identifiation and Accommodation for Incipient and Abrupt Faults 46th IEEE Conference on Decision and Control  CDC2007 New Orleans, LA, USA, December 12-14, 2007. 

 

D. D'Alessandro, G.Parlangeli, F. Albertini Non-stationary quantum walks on the cycle Phys. A: Math. Theor. 40 (2007)  14447-14455.

 Manni, G.Parlangeli, M.L.Corradini Robust stabilization of nonlinear sandwich plants containing generalized hysteresis nonlinearities , 17th IFACWorld Congress July 6-11, 2008, Seoul

 

 

 

 

 

 M. L. Corradini, G. Sammarco, A. Manni,G.ParlangeliVRL, a novel environment for control engineering practicing: an application to a fault tolerant control system, Ninth International Conference on Control, Automation, Robotics Vision, 5 - 8 December 2006, Grand Hyatt Singapore.

 

 A. Bicchi, A. Marigo, G. Pappas, M. Pardini, G. Parlangeli, C. Tomlin, and S. Sastry. Decentralized Air Traffic Management Systems: Performance and Fault Tolerance. In Proc. IFAC Workshop on Motion Control, , 21-23 September 1998, Grenoble, France.

L. Pallottino,G. Parlangeli and A. Bicchi. Shortest paths for teams of vehicles. In Proc. WAC Int. Symp. on Robotics and Applications, Maui, Hawaii June 11-16, 2000.

G. Parlangeli, M.E. Valcher Disturbed Fault Detection and Isolation Problems for Linear State Models in a Noisy Environment 15th IFAC World Congress on Automatic Control Barcelona, Spain July 21-26, 2002.

M.L. Corradini, G. Parlangeli Robust Stabilization of Nonlinear Uncertain Plants with Hysteresis in the Actuator: a Sliding Mode Approach 2002 IEEE Int. Conf. on Systems, Man and Cybernetics Hammamet, Tunisia, October 6-9, 2002.

M. L. Corradini and G. Parlangeli Dynamic output feedback variable structure control for the output stabilization of MIMO uncertain plants with actuator and sensor hysteresis nonlinearities 4th IFAC Symposium on Robust Control Design (ROCOND 2003), Milan, 25-27 June 2003.

G. Parlangeli, M.E. Valcher LQ Optimal Control in a Behavioral Setting: new Perspectives on the Problem Statement and Solution European Control Conference ECC 2003 , University of Cambridge, UK, 1-4 September 2003.

M.L. Corradini, G. Orlando and G. Parlangeli Robust stabilization of nonlinear plants with uncertain hysteresis-like actuator nonlinearities European Control Conference ECC 2003 , University of Cambridge, UK, 1-4 September 2003.

 G. Parlangeli, M.E. Valcher Optimal Filtering, Fault Detection and Isolation for Linear Discrete Time Systems in a Noisy Environment, Dicembre 2003, International Journal of Adaptive Control and Signal Processing.

M.L. Corradini andG. Parlangeli A fault tolerant control system for the output stabilization of SISO plants with actuator uncertain hysteresis nonlinearities 42nd IEEE Conference on Decision and Control  Hawaii, USA, December 9-12, 2003.

G. Parlangeli, M.E. Valcher A behavioral approach to the LQ optimal control problem Mathematical Theory of Networks and Systems (MTNS2004) Katholieke Universiteit Leuven, Belgium , July 5-9, 2004.

 M.L. Corradini, G.Orlando,G.Parlangeli A VSC Approach for the Robust Stabilization of Nonlinear Plants with Uncertain non-smooth actuator Nonlinearities - a Unified Framework, IEEE Trans. on Automatic Control, vol. 49, n.5 Maggio 2004.

 M.L. Corradini,G.Parlangeli Output zeroing of MIMO plants in the presence of actuator and sensor uncertain hysteresis nonlinearities, accepted as regular paper 43rd IEEE Conference on Decision and Control (CDC 2004), December 14-17,2004 Atlantis, Paradise Island, Bahamas.

 M.L. Corradini,G.Parlangeli Output zeroing of MIMO plants in the presence of actuator and sensor uncertain hysteresis nonlinearities, IEEE Trans. on Automatic Control Volume 50, Issue 9, Sept. 2005 Page(s):1403 - 1407

M.L. Corradini, G.Orlando,G.Parlangeli An observer-based fault-accommodating controller for nonlinear systems in the presence of sensor failures, paper 16th IFAC World Congress July 4-8, 2005 Prague, Czech Republic.

M.L. Corradini, Orlando, G., G.Parlangeli Robust control of nonlinear uncertain systems with sandwiched backlash, 44th IEEE Conference on Decision and Control (CDC-ECC 2005), December 12-15, 2005 Seville, Spain.

M.L. Corradini, Orlando, G., G.Parlangeli A fault tolerant sliding mode controller for accommodating actuator failures, 44th IEEE Conference on Decision and Control (CDC-ECC 2005) , December 12-15, 2005 Seville, Spain.

Indiveri G.,G.Parlangeli On thruster allocation, fault detection and accommodation issues for underwater robotic vehicles, IEEE ISCCSP 2006 Second International Symposium on Communications, Control, and Signal Processing, March 13-15, 2006 Marrakech, Morocco.

 

M.L. Corradini, G. Orlando, G.Parlangeli Actuator Failures Compensation: a sliding mode approach, IEEE MED 2006 14th Mediterranean, Conference on Control and Automation, June 28 - 30 , 2006 Ancona, Italy.

 M.L. Corradini, G. Orlando,G.Parlangeli Robust stabilization of linear unstable plants with saturating actuators using a time varying sliding surface: preliminary results, 9th International Workshop on Variable Structure Systems, June 5 - 7 , 2006 Alghero, Italy.

 

Temi di ricerca

 

Sistemi di controllo robusto a struttura variabile con sliding mode.

Diagnostica di guasti per sistemi dinamici e e riconfigurazione del controllo in presenza di guasti

Sistemi multiagente

Pianificazione delle traiettorie per robot mobili