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Course
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Scientific Disciplinary Sector Code
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Contact Hours
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Exercise Hours
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Laboratory Hours
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Type of Activity
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Language
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20802124 -
CONTROL AND SYSTEMS THEORY
(objectives)
Provide to the students methodologies and techniques for the analysis and modeling of linear time-invariant systems by focusing on the state-space representation. Provide the knowledge for the design of feedback control systems. Derive the state-space model of Multi-Input Multi-Output systems. Provide the knowledge of the structural properties of MIMO dynamical models and the asymptotic observer for the eigenvalue assignment problem and the regulation problem. Provide the students with basic concepts for the analysis of non linear system.
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20802124-1 -
TEORIA DEI SISTEMI E DEL CONTROLLO MODULO I
(objectives)
Provide to the students methodologies and techniques for the analysis and modeling of linear time-invariant systems by focusing on the state-space representation. Provide the knowledge for the design of feedback control systems. Derive the state-space model of Multi-Input Multi-Output systems. Provide the knowledge of the structural properties of MIMO dynamical models and the asymptotic observer for the eigenvalue assignment problem and the regulation problem. Provide the students with basic concepts for the analysis of nonlinear system.
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GASPARRI ANDREA
( syllabus)
Linear Systems
1. INTRODUCTION TO LINEAR SYSTEMS
1.1. Modelling
1.2. State-Space Representation
2. NONLINEAR SYSTEMS AND LINEARIZATION
2.1. Motivations and Definitions
2.2. Linearization Method
3. DIFFERENTIAL EQUATIONS
3.1. Linear Differential Equations with Constant Coefficients
3.2. Exponential Matrix
3.3. Free Evolution
3.4. Forced Evolution
4. RELATIONSHIP BETWEEN REPRESENTATIONS
4.1. From State-Spate to Transfer Function
4.2. From Transfer Function to State-Spate
5. MODAL DECOMPOSITION
5.1. Eigenvalues and Eigenvectors
5.2. Coordinate Transformation
5.3. Diagonalization and Jordanization
6. STRUCTURAL PROPERTIES
6.1. Controllability and Observability
6.2. Controllability and Observability Kalman Forms
6.3. Kalman Canonical Decomposition
7. REALIZATION AND CANONICAL FORMS
7.1. Realization
7.2. Canonical Forms for Realization
( reference books)
1. An Introduction to Linear Control Systems, Thomas E. Fortmann, Konrad L. Hitz
2. Lecture Notes (http://gasparri.dia.uniroma3.it/Stuff/complementi_teoria_dei_sistemi.pdf)
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6
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ING-INF/04
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54
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Core compulsory activities
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ITA |
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20802124-2 -
TEORIA DEI SISTEMI E DEL CONTROLLO MODULO II
(objectives)
Provide to the students methodologies and techniques for the analysis and modeling of linear time-invariant systems by focusing on the state-space representation. Provide the knowledge for the design of feedback control systems. Derive the state-space model of Multi-Input Multi-Output systems. Provide the knowledge of the structural properties of MIMO dynamical models and the asymptotic observer for the eigenvalue assignment problem and the regulation problem. Provide the students with basic concepts for the analysis of nonlinear system.
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GASPARRI ANDREA
( syllabus)
Linear Systems
1. EIGENVALUE ASSIGNMENT PROBLEM
1.1. Eigenvalue assignment using state feedback
1.1.1. Assignment Theorem (SISO/MIMO)
1.1.2. Assignment Unicity Theorem (SISO)
1.2. Stabilization Problem
1.3. State Asymptotic Observer
1.4. Separation Principle
1.5. Eigenvalue placement using output feedback
2. LINEAR OUTPUT REGULATION PROBLEM
2.1. Full-Information Problem
2.2. Error-Feedback Problem
3. ROOT LOCUS
2.8. Exact Root Locus Analysis
2.9. Approximate Root Locus Analysis
2.10. Root Locus Design
Nonlinear Systems
1. INTRODUCTION TO NONLINEAR SYSTEMS
1.1. Fundamental Properties
1.2. Lipschitz Condition
1.3. Existence and Unicity of Solution
1.4. Comparison Lemma
2. LYAPUNOV STABILITY
2.1. Autonomous Systems
2.2. Stability Definition
2.3. Stability Theorem (Direct Criterion)
2.4. Chetaev Instability Theorem
2.5. Lyapunov Control Functions (Krasovskii)
2.6. Invariance Principle (LaSalla Theorem)
2.7. Stability Theorem for Linear Systems (Indirect Criterion)
( reference books)
Main Textbook:
1. Sistemi di controllo (Vol. 2), Alberto Isidori
2. Lecture Notes (http://gasparri.dia.uniroma3.it/Stuff/complementi_teoria_dei_sistemi.pdf)
Main Textbook:
1. Nonlinear Systems (3rd Edition), Hassan K. Khalil
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6
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ING-INF/04
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54
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-
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Core compulsory activities
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ITA |
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20802112 -
SIMULATION OF INDUSTRIAL AND LOGISTIC PROCESSES
(objectives)
It gives a formal instruments to model information flows and to optimize the operation management of production systems, in particular of flexible manufacturing systems.
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ADACHER LUDOVICA
( syllabus)
SCHEDULING
CONTROLLO DELLE OPERAZIONI SU UNA MACCHINA
EDD,SPT,MOORE, SMITH, SMITH MODIFICATO, LAWLER
CONTROLLO DELLE OPERAZIONI NELLE CELLE
GRAFO DEGLI STATI, CONFLITTI, PROGRAMMAZIONE DINAMICA E A*.
CONTROLLO DELLE OPERAZIONI NELLE LINEE
ALGORITMO DI JOHNSON PER IL SEQUENZIAMENTO SU DUE MACCHINE
APPLICAZIONE DELL'ALGORITMO DI GILMORE E GOMORY A LINEE DI DUE MACCHINE SENZA ATTESA INTERMEDIA
MINIMO RITARDO MASSIMO CON TEMPO DI RILASCIO POSITIVO E INTERRUZIONE: 'BRANCH AND BOUND"; GRAFO DISGIUNTIVO PER IL JOB SHOP ("CLIQUE" DI MACCHINE)
SEQUENZIAMENTO DI MACCHINA SPOSTANDO IL COLLO DI BOTTIGLIA: EURISTICA RISOLUTIVA PER IL JOB SHOP ("SHIFTING BOTTLENECK")
SIMULAZIONE
LA SIMULAZIONE AD EVENTI DISCRETI, METODOLOGIA FONDAMENTALE PER LA VALUTAZIONE DELLE PRESTAZIONI DI SISTEMI COMPLESSI (DI CALCOLO, DI TELECOMUNICAZIONE, DI TRAFFICO, ECC) È LA MATERIA SU CUI VERTE QUESTO CORSO. PUR ESSENDO DI CARATTERE INTRODUTTIVO, IL CORSO HA COME OBIETTIVO DI RENDERE LO STUDENTE IN GRADO DI AFFRONTARE LO STUDIO DI CASI REALI AVENDO CONOSCENZA DEL METODO DA SEGUIRE E DELLE POTENZIALITÀ DELLE TECNICHE DISPONIBILI
GLI ARGOMENTI TRATTATI POSSONO ESSERE RAGGRUPPATI NEI SEGUENTI TRE FASI:
O COSTRUZIONE DI UN MODELLO DI UN SISTEMA REALE:
VERRANNO DISCUSSI I CONCETTI DI LIVELLO DI ASTRAZIONE E ADEGUATEZZA DI UN MODELLO, E ILLUSTRATE ALCUNE METODOLOGIE PER LA COSTRUZIONE DEI MODELLI. GLI ESEMPI VERRANNO SVILUPPATI UTILIZZANDO DUE FORMALISMI MOLTO NOTI: LE RETI DI CODE E LE RETI DI PETRI. SARANNO INOLTRE DISCUSSE ALCUNE SEMPLICI LEGGI OPERAZIONALI CHE SERVONO PER LA DEFINIZIONE DEGLI INDICI DI PRESTAZIONE DEI MODELLI.
O "ESECUZIONE" DI UN MODELLO DI SIMULAZIONE
VERRÀ SPIEGATO COSA SIGNIFICA ESEGUIRE UN MODELLO DI SIMULAZIONE E COME SI PUÒ REALIZZARE UN PROGRAMMA DI SIMULAZIONE AD EVENTI DISCRETI. I MODELLI DI SIMULAZIONE CHE SARANNO TRATTATI SONO MODELLI PROBABILISTICI, OVVERO MODELLI LA CUI EVOLUZIONE È GOVERNATA DA LEGGI CASUALI. QUESTO RICHIEDERÀ UN RICHIAMO DEI FONDAMENTI DI CALCOLO DELLE PROBABILITÀ. VERRANNO INOLTRE PRESENTATI METODI PER LA GENERAZIONE DI ISTANZE DI VARIABILI CASUALI.
O INTERPRETAZIONE DEI RISULTATI DELLA SIMULAZIONE:
I RISULTATI PRODOTTI DA UN SIMULATORE COSTITUISCONO LE COMPONENTI DI UN CAMPIONE STATISTICO E COME TALI DEVONO ESSERE UTILIZZATI PER LA CONFERMA DELLA LORO VALIDITÀ. IL CORSO INCLUDE IL RICHIAMO DI ALCUNI ELEMENTI FONDAMENTALI DI STATISTICA UTILI PER LA PRESENTAZIONE DEI METODI CHE PERMETTONO LA STIMA INTERVALLARE DEGLI INDICI DI PRESTAZIONE DEI MODELLI STUDIATI.
SARÀ RICHIESTO AGLI STUDENTI DI SVOLGERE DEGLI ESERCIZI PRATICI PER VERIFICARE LA COMPRENSIONE DI QUANTO ESPOSTO A LEZIONE. GLI STUDENTI DOVRANNO MOSTRARE SIA CAPACITÀ DI ANALISI DI PROBLEMI REALI E IMPOSTAZIONE DI ALGORITMI RISOLUTIVI IN VIA SIMULATIVA, SIA CAPACITÀ OPERATIVE DI PROGRAMMAZIONE CON LINGUAGGI STANDARD (C, JAVA).
( reference books)
slides on home page of professor
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9
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ING-INF/04
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81
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Core compulsory activities
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ITA |
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20801764 -
COMBINATORIAL OPTIMISATION
(objectives)
The course aims at providing basic methodological and operative knowledge to represent and cope with decision processes and quantitative models.
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NICOSIA GAIA
( syllabus)
Introduction to Combinatorial Optimization. Optimizations Algorithms. Computational complexity analysis of algorithms.
Computational Complexity: Decision vs. optimization problems. Classes P and NP. Polynomial reductions. NP-complete problems. Pseudo-polynomial algorithms.
Approximation algorithms: approximation classes (NPO, APX, PTAS, FPTAS, PO). Approximation algorithm for Vertex Cover: greedy, DFS, LP based algorithms (LP rounding and Primal Dual). 0-1 Knapsack problem: NP-completeness, DP algorithms, greedy algorithm, polynomial time approximation scheme, fully polynomial time approximation scheme.The travelling salesman problem, TSP: NP-completeness, non approximability result, study of real world applications, the metric TSP, approximation algorithms for the metric TSP, the TSPP. Scheduling on parallel machines: approximation algorithms.
Heuristic algorithms: constructive algortihms (for the TSP: inserition heuristics, geometric algorithms); local serach (for the TSP: 2-opt exchange, 3-opt, k-opt, OR-opt), variable depth method of local search (Lin-Kernigan algorithms), tabu search, simulated annealing, genetic algorithms.
Introduction to online algorithms.
( reference books)
[1] “Lecture Notes on Approximation Algorithms”, Volume I, R. Motwani.
[2] Lecture notes by prof. Agnetis (in italian: computational complexity, TSP, euristic algorithms).
[3] G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, M. Protasi, "Complexity and Approximation,Combinatorial optimization problems and their approximability properties", Springer Verlag, 1999
[4] Slides of the lectures available on the web page of the course
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6
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MAT/09
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54
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Related or supplementary learning activities
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ITA |
Optional group:
GESTIONALE Orientamento unico CURRICULUM GESTIONALE UNO A SCELTA - (show)
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6
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20801761 -
ELEMENTS OF ORGANISATION
(objectives)
Provide the notions and develop the logics necessary to understand the formal description and the actual functioning of firms and institutions, and their evolutionary tendencies related to the evolution of their operating environment. Introducing to organizational analysis, bringing the student to be able to think about the relationships between market, strategy, structure and processes from a total quality perspective and taking into account people's organizational behaviors and motivations.
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PROTTO STEFANO
( syllabus)
• 1. INTRODUCTION
1.1. General topics and definitions
1.2. Historical overview
1.3. Dynamic and static models
1.4. Effects of instability, Burns & Stalker's model
1.4. Current Scenario and evolutionary trends
• 2. STRUCTURES
2.1. Definitions
2.2. Delegation/Proxy concept
2.3. Structuring resources, types of Organizational Structures and operation
2.4. Committees
2.5. Organizational structures and structural evolution: Mintzberg's view
2.6. Hierarchical Line: Jaques' view
2.7. Organizational Structure vs processes
2.8. Networks
• 3 MOTIVATION
3.1. The human needs according to Maslow and Herzberg
3.2. Motivation and demotivation
3.3. The human work according to Jaques
3.4. Relationship between individual and organization
3.5. Psychological issues of delegation
• 4 ORGANIZATIONAL CULTURE
4.1. Spontaneous (local) groups
4.2. Development of a culture within a spontaneous group
4.3. Work teams
4.4. The organizational structure as an aggregate of groups, analysis of its culture
• 5 HUMAN RESOURCES MANAGEMENT AND DEVELOPMENT
5.1. Organizational functions related to H.R. management and development
5.2. Selecting, Evaluating, Educating, career and replacement planning
5.3. Compensation planning
• 6 MICROORGANIZATION
6.1. Organization Documentation
6.2. Documenting the organizational structure
6.3. Procedures and processes
6.4. Organizational analysis
• ORGANIZATION AND ENTREPRENEURSHIP LABORATORY (choice of topics for group work; the first two topics are anyway treated; not treated topics are put out of program)
– INFORMATION SYSTEMS
1. Organization and information integration
2. Information as empowering factor
3. Resourses, processes and informative flows
4. IT architectures and systems
5. Computing integration and ERP systems
– QUALITY SYSTEMS
1. The Quality concept and its historical evolution
2. From Quality Control to Total Quality, organization and tools
3. ISO9000 norms and certification
4. EFQM
– PRODUCT VS MARKET ANALYSIS
1. Strategy and strategic planning
2. Competitive Positioning, SBA and SBU
3. The Boston and Mc Kinsey growth–share matrixes, portfolio analysis
4. SWOT analysis
– PROCESSES
1. Process concept
2. Process modelling
3. Process evaluation
4. Process reengineering
– BUSINESS PLAN
1. Scopes and usefulness
2. Structure
3. Development
4. Contents and analysis tools
– AUTHORITY AND POWER
1. Authority and power concepts
2. Bureaucracies and boss-worker relation (Jaques)
3. Interactions within a social network with structure and roles (Jaques)
4. Basis, characteristics and practice of power in the organizations (Mintzberg)
5. Alliances
( reference books)
SLIDE PRESENTATIONS OF LECTURES
INDIVIDUAL BIBLIOGRAPHIC SEARCHES
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6
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ING-INF/04
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54
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Core compulsory activities
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ITA |
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Optional group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
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6
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20801758 -
DATABASES I
(objectives)
Presentation of models, methods and tools for the definition, design and development of software systems that manage large sets of data. A student who has passed the course will be able to: (i) develop software applications that make use of databases of even high complexity, (i) design and built autonomously databases of medium complexity, and (iii) be involved in the project and development of large databases of high complexity.
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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20810087 -
MACHINE LEARNING
(objectives)
Enable students to deepen the main Machine Learning models and methods, such as Regression, Classification, Clustering, Deep Learning, and use them as tools for the development of innovative technologies.
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Derived from
20810087 MACHINE LEARNING in Ingegneria informatica LM-32 MICARELLI ALESSANDRO
( syllabus)
1. Regression
Review of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso
2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes
3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval
4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems
5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications
6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.
( reference books)
Lecture notes by the professor.
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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Università Roma Tre