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Course
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Credits
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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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Personal Study Hours
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Type of Activity
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Language
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20810528 -
SYSTEM AND CONTROL 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 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. DIFFERENTIAL EQUATIONS
2.1. Linear Differential Equations with Constant Coefficients
2.2. Exponential Matrix
2.3. Free Evolution
2.4. Forced Evolution
3. RELATIONSHIP BETWEEN REPRESENTATIONS
3.1. From State-Spate to Transfer Function
3.2. From Transfer Function to State-Spate
4. MODAL DECOMPOSITION
4.1. Eigenvalues and Eigenvectors
4.2. Coordinate Transformation
4.3. Diagonalization and Jordanization
5. STRUCTURAL PROPERTIES
5.1. Controllability and Observability
5.2. Controllability and Observability Kalman Forms
5.3. Kalman Canonical Decomposition
7. EIGENVALUE ASSIGNMENT PROBLEM
7.1. Eigenvalue assignment using state feedback
7.1.1. Assignment Theorem (SISO/MIMO)
7.1.2. Assignment Unicity Theorem (SISO)
7.2. Stabilization Problem
7.3. State Asymptotic Observer
7.4. Separation Principle
7.5. Eigenvalue placement using output feedback
8. LINEAR OUTPUT REGULATION PROBLEM
8.1. Full-Information Problem
8.2. Error-Feedback Problem
Nonlinear Systems
9. INTRODUCTION TO NONLINEAR SYSTEMS
9.1. Fundamental Properties
9.2. Lipschitz Condition
9.3. Existence and Unicity of Solution
9.4. Comparison Lemma
10. LYAPUNOV STABILITY
10.1. Autonomous Systems
10.2. Stability Definition
10.3. Stability Theorem (Direct Criterion)
10.4. Chetaev Instability Theorem
10.5. Lyapunov Control Functions (Krasovskii)
10.6. Invariance Principle (LaSalla Theorem)
10.7. Stability Theorem for Linear Systems (Indirect Criterion)
( reference books)
Linear Systems
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)
3. Sistemi di controllo (Vol. 2), Alberto Isidori
Nonlinear Systems
1. Nonlinear Systems (3rd Edition), Hassan K. Khalil
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9
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ING-INF/04
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81
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-
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-
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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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9
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ING-INF/04
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81
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-
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Core compulsory activities
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ITA |
Optional group:
Curriculum Automazione dei Sistemi Complessi: I ANNO uno a scelta tra tre insegnamenti - (show)
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6
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20810534 -
CONTROL MEASURES AND TECHNOLOGIES
(objectives)
Present the main aspects of the measures and technologies to build modern control systems based on transductors, data extraction and data processing.
To present, in particular, processing of sensory data, estimation techniques for auto and cross-correlation, test signal generation, FFT based harmonic response estimation, as well as the techniques and components at the basis of the actuators of electric engines.
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6
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ING-IND/32
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54
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-
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-
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Related or supplementary learning activities
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ITA |
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Optional group:
Curriculum Automazione dei Sistemi Complessi I anno : uno a scelta tra quattro insegnamenti - (show)
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6
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20810322 -
Artificial intelligence e machine learning
(objectives)
The goal is to present the fundamental models, methods and techniques of some relevant areas of Artificial Intelligence, with particular reference to heuristic search and Machine Learning, and to use them as tools for the development of innovative technologies. As for Machine Learning, the course will allow students to learn the main methods and algorithms typical of the discipline (supervised, unsupervised and with reinforcement). The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
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SANSONETTI GIUSEPPE
( syllabus)
1. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
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MICARELLI ALESSANDRO
( syllabus)
. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
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6
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ING-INF/05
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54
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-
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-
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Core compulsory activities
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ITA |
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20801961 -
OPERATING SYSTEMS
(objectives)
The course intend to provide: (1) competencies about a generic modern operating system, (2) competencies about the structure of a unix operating system, and specifically about linux, (3) knowledge about methodologies adopted for solving problems within the management of a modern operating system, (4) ability in the use a unix platform as a user, (5) ability in programming a unix system (scripting), (6) basic ability in system programming
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6
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ING-INF/05
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54
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-
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-
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Core compulsory activities
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ITA |
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20801686 -
DATABASES
(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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-
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Core compulsory activities
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ITA |
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Optional group:
Curriculum Automazione dei Sistemi Complessi: I ANNO due a scelta tra quattro insegnamenti - (show)
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12
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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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6
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ING-INF/04
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54
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-
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-
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Core compulsory activities
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ITA |
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Optional group:
Curriculum Automazione dei Sistemi Complessi: I ANNO uno a scelta tra due insegnamenti - (show)
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9
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20810399 -
Company Economics and Strategy
(objectives)
Basic knowledge of the financial economic language by which the internal and external company relations and strategies are expressed. Developing of economic and financial evaluation of company operations. Analysis of interactions among internal organizational structures and analysis of financial and economic results drivers and measures.
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9
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ING-IND/35
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81
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-
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Related or supplementary learning activities
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ITA |
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20801715 -
MACHINES AND ELECTRIC OPERATIONS
(objectives)
The course has the purpose to describe the manufacturing features and the functional characteristics of the main rotating electrical machines, including dynamic models used for the study of the electrical machine behavior in electromechanical systems. It is expected that the student will acquire the ability to select the various electromechanical equipment used in industrial applications or in power systems for the electric energy generation. The course gives basic knowledge concerning the main configurations of the power electronic converters that are used for the control of power supply of electrical machines as well as it gives basic knowledge of the main algorithms being used in electric drives for control and monitoring of the machine performance. As a result, the course is targeted to give the know-how concerning how to select main design characteristics of an electric drive in connection with the functional specification of a given application.
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Derived from
20801715 MACCHINE E AZIONAMENTI ELETTRICI in Ingegneria aeronautica LM-20 LIDOZZI ALESSANDRO
( syllabus)
Short introduction to electrical drives: functional block schemes, review on electrical machine types, AC and DC, quadrants of operations, regenerative brake, thermal behaviors, analysis in the Laplace domain.
Power electronics converters for electrical drives: topologies, characteristics and modulation techniques.
Short view on DC electric drives: torque and flux control, speed control and position control.
Electrical drives based on synchronous machine: electrical machine operation and drive block scheme, dynamic model in the synchronous reference frame, operation under sinusoidal supply, control strategies for the related electrical drives.
Electrical drives with induction machine: electrical machine operation and drive block scheme, dynamic model in the synchronous reference frame, scalar and vector control strategies for induction machine based electrical drives.
( reference books)
In addition to the lecture notes provided by the teacher:
Ion Boldea, Syed A. Nasar
Electric Drives, Third Edition 2016 by CRC Press
ISBN 9781498748209
Bimal K. Bose
Modern Power Electronics and AC Drives
Prentice Hall PTR, 2002
Ned Mohan, Tore M. Undeland, William P. Robbins
Power Electronics: Converters, Applications, and Design
ISBN: 0471226939
Ned Mohan
Advanced Electric Drives: Analysis, Control, and Modeling Using MATLAB / Simulink
ISBN: 978-1-118-48548-4
Ned Mohan
Electric Drives: An Integrative Approach
ISBN: 0971529256
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9
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ING-IND/32
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81
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-
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-
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Related or supplementary learning activities
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ITA |
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Università Roma Tre