UNIVERSITA' DEGLI STUDI ROMA "TOR VERGATA"| 2 YEAR | II semester | 6 CFU |
Eugenio Martinelli |
A.Y. 2024-25 new
📑
|
| Didatticaweb | |
| Code: SSD: ING-INF/01 |
Deep Learnig and Applications – 6 CFU (opt C1.b)
II semester
Electronic Interfaces – 6 CFU (block E, optB, optC1.b, optC2.b)
| 1 YEAR | II semester | 6 CFU |
| Christian Falconi | A.Y. 2022-23 (since) |
| since A.Y. 2023-24 (new block E) – program | |
| Code: 80300103 SSD: ING-INF/01 |
FORMATIVE OBJECTIVES
LEARNING OUTCOMES:
The goal is to teach the fundamental principles and tools for designing electronic interfaces.
The contents of the course have general validity, but the focus will be on electronic interfaces for mechatronics.
The course is oriented toward design.
KNOWLEDGE AND UNDERSTANDING:
Students will need to know and understand the fundamental principles and tools for the analysis and design of electronic interfaces.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Students will have to demonstrate that they are able to design electronic interfaces.
MAKING JUDGEMENTS:
Students will be able to evaluate the design of electronic interfaces.
COMMUNICATION SKILLS:
The students, in addition to illustrating the fundamental principles and tools for the design of electronic interfaces, must be able to explain each design choice.
LEARNING SKILLS:
Students must be able to read and understand scientific texts and articles (also in English) concerning electronic interfaces.
PREREQUISITES
Thévenin equivalent circuit.
Norton equivalent circuit.
Laplace transform
Fourier transform
Syllabus:
Fundamentals on electronic devices.
Equivalent circuits (mechanic systems, thermal systems,…).
Diode circuits.
Transistor circuits.
Nullors.
Operational amplifiers (op amps).
Universal active devices.
Non-idealities of op-amps and other universal active devices.
Op-amp circuits.
Simulations of electronic circuits (SPICE).
Electronic interfaces.
Circuits for mechatronics (design examples).
Adaptive Systems (block C-opt) –> Identification and Neural Networks (24-25)
| 2 YEAR | II semester | 6 CFU |
| Patrizio Tomei (4cfu) Eugenio Martinelli (2cfu) |
A.Y. 2023-24 |
| SANTOSUOSSO Giovanni Luca | A.Y. 2024-25 not be activated |
| A.Y. 2025-26 (new name “Identification and Neural Networks” |
|
| Didatticaweb | |
| Code: 80300088 SSD: ING-INF/04 |
Pre-requirement: The basics of systems theory and control are required.
LEARNING OUTCOMES: The course aims to provide the basic techniques for the design of predictors, filters, and adaptive controllers.
KNOWLEDGE AND UNDERSTANDING: Students must obtain a detailed understanding of design techniques with the help of MATLAB-SIMULINK to solve industrial problems of adaptive filtering, adaptive prediction, and adaptive control.
APPLYING KNOWLEDGE AND UNDERSTANDING: Students must be able to apply the project techniques learned in the course even in different industrial situations than those examined in the various phases of the course.
MAKING JUDGEMENTS: Students must be able to apply the appropriate design technique to the specific cases examined, choosing the most effective algorithms.
COMMUNICATION SKILLS: Students must be able to communicate using the terminology used for filtering, prediction, and adaptive control. They must also be able to provide logical and progressive exposures starting from the basics, from structural properties, from modeling to the design of algorithms, without requiring particular prerequisites. Students are believed to be able to understand the main results of a technical publication on the course topics. Guided individual projects (which include the use of Matlab-Simulink) require assiduous participation and exchange of ideas.
LEARNING SKILLS: Students must be able to identify the appropriate techniques and algorithms in real cases that arise in industrial applications. Furthermore, it is believed that students have the ability to modify the algorithms learned during the course in order to adapt them to particular situations under consideration.
Texts
Adaptive Filtering Prediction and Control, Graham C. Goodwin, Kwai Sang Sin, Dover Publications, 2009.
Control of Electrical Machines (B-C-E) –> CONTROL OF ELECTRICAL MOTORS AND VEHICLES (B-C1-C2-E) (25-26)
| 2 YEAR | II semester | 6 CFU |
| Cristiano M. Verrelli | A.Y. 2021-22 to A.Y. 2024-25 |
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A.Y. 2025-26 (new name CONTROL OF ELECTRICAL MOTORS AND VEHICLES ) |
| Code:8039782 SSD: ING-INF/04 |
LEARNING OUTCOMES: The course aims to provide a unified exposition of the most important steps and concerns in mathematical modeling and design of estimation and control algorithms for electrical machines such as:
– permanent magnet synchronous motors
– permanent magnet stepper motors
– synchronous motors with damping windings
– induction (asynchronous) motors
– synchronous generators.
KNOWLEDGE AND UNDERSTANDING: Students should be able to gain profound insight into the fundamental mathematical modeling and control design techniques for electrical machines, which are of interest and value not only to engineers engaged in the control of electric machines but also to a broader audience interested in (nonlinear) control design.
APPLYING KNOWLEDGE AND UNDERSTANDING: Students should be able to deeply understand mathematical modeling through nonlinear differential equations, stability and nonlinear control theory concepts, and design of (nonlinear) adaptive controls containing parameter estimation algorithms (important for applications). Students should be able to apply the related knowledge to learning control of robotic manipulators and cruise/yaw rate control of electric vehicles.
MAKING JUDGEMENTS: Students should be able to identify the specific design scenario and apply the most suitable techniques. Students should be able to compare the effectiveness of different controls while analyzing theoretical/experimental advantages and drawbacks.
COMMUNICATION SKILLS: Students should be able to use a single notation and modern (nonlinear) control terminology. Students should be able to exhibit a logical and progressive exposition starting from basic assumptions, structural properties, modeling, control, and estimation algorithms. Students are also expected to be able to read and capture the main results of a technical paper concerning the topics of the course, as well as to effectively communicate in a precise and clear way the content of the course. Tutor-guided individual projects (including Maple and Matlab-Simulink computer simulations and lab visits) invite intensive participation and exchanging ideas.
LEARNING SKILLS: Being enough skilled in the specific field to undertake the following studies characterized by a high degree of autonomy.
TEXTS
R. Marino, P. Tomei, C.M. Verrelli, Induction Motor Control Design, Springer, 2010.
Latest journal papers.
VERIFICATION OF THE KNOWLEDGE
Verify the knowledge and skills acquired by the student on the topics covered by the program. The intermediate exams, the final written tests, and the oral exam will consist of questions related to the topics covered by the program of the course. The questions are aimed at ascertaining the student’s knowledge and his/her reasoning skills in making logical connections between the different topics.
The final vote of the exam is expressed out of thirty and will be obtained through the following graduation system:
Not pass: important deficiencies in the knowledge and in the understanding of the topics; limited capacity for analysis and synthesis, frequent mistakes and limited critical and judgmental capacity, inconsistent reasoning, inappropriate language.
18-21: the student has acquired the basic concepts of the discipline and has an analytical capacity that comes out only with the help of the teacher. The way of speaking and the language used are almost correct, though not precise.
22-25: the student has acquired the basic concepts of the discipline in a discrete way; he/she knows how to discuss the various topics; he/she has an autonomous analysis capacity while adopting a correct language.
26-29: the student has a well-structured knowledge base. He/She is able to independently adopt a correct logical reasoning; notations and technical language are correct.
30 and 30 cum laude: the student has a complete and in-depth knowledge base. The cultural references are rich and up-to-date while being expressed by means of brilliant technical language.
MEASUREMENT SYSTEMS FOR MECHATRONICS – 6 cfu (2023-24 last year)
| 2 YEAR | II semester | 6 CFU |
| Arianna Mencattini | A.Y. 2021-22
A.Y. 2022-23 |
| A.Y. 2023-24 Measurement Systems for Mechatronics
A.Y. 2024-25: Computer Vision – program |
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| Code: 8039787 SSD: ING/INF/07 |
LEARNING OUTCOMES: Learning basic concepts in digital image processing and analysis as a novel measurement system in biomedical fields. The main algorithms will be illustrated particularly devoted to the image medical fields.
KNOWLEDGE AND UNDERSTANDING: The student acquires knowledge related to the possibility to use an image analysis platform to monitor the dynamics of a given phenomenon and to extract quantitative information from digital images such as object localization and tracking in digital videos.
APPLYING KNOWLEDGE AND UNDERSTANDING: The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course with the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.
MAKING JUDGEMENTS: :
The student must be able to integrate the basic knowledge provided with those deriving from the other courses such as probability, signal theory, and pattern recognition. some fundamentals of measurement systems as well as basic metrological definitions will be provided in support of background knowledge.
COMMUNICATION SKILLS:
The student solves a written test and develops a project in Matlab that illustrates during the oral exam. The project can be done in a group to demonstrate working group capabilities.
LEARNING SKILLS:
Students will be able to read and understand scientific papers and books in English and also to deepen some topics. In some cases, students will develop also experimental tests with time-lapse microscopy acquisition in the department laboratory.
SYLLABUS:
Fundamentals of metrology. Basic definitions: resolution, accuracy, precision, reproducibility, and their impact over an image based measurement system. Image processing introduction. Image representation. Spatial and pixel resolution. Image restoration. Deconvolution. Deblurring. Image quality assessment. Image enhancement. Image filtering for smoothing and sharpening. Image segmentation: pixel based (otsu method), edge based, region based (region growing), model based (active contour, Hough transform), semantic segmentation. Morphological operators. Object recognition and image classification. Case study: defects detection, object tracking in biology, computer assisted diagnosis, facial expression in human computer interface.
Matlab exercises.
Machine Design – 6 CFU (block A)
| 2 YEAR | II semester | 6 CFU |
| Luciano CANTONE | since A.Y. 2018-19 – program 📑 |
| (by Engineering Sciences) | |
| Code: 80300065 SSD:ING-IND/14 |
POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY – 9 CFU
| 1 YEAR (Block C)
2 YEAR (Blocks A|B|D|E) |
II semester | 9 CFU |
| Stefano CORDINER (6/9 cfu) Lorenzo BARTOLUCCI (3/9 cfu) |
A.Y. 2021-22
Internal Combustion Engines |
| Since A.Y. 2022-23 – program 📑
POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY |
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| Code: 80300079
80300077 M-6264 |
PREREQUISITES: Technical Physics, Fluid Machinery
FORMATIVE OBJECTIVES
LEARNING OUTCOMES:
The course aims to provide students with in-depth scientific training to correctly address the problems of designing, choosing and managing new propulsion systems for sustainable mobility starting from current solutions with internal combustion engines as well as creating the conditions for the development of innovative and low environmental impact solutions. To this end, students will develop in-depth knowledge of the operating principles of propulsion systems for transport and will learn simulation procedures for their verification and sizing. Finally, particular attention is dedicated to the most recent technological development of internal combustion engine technology aimed at overcoming current limits in terms of emissions and efficiency and defining innovative scenarios for sustainable mobility.
KNOWLEDGE AND UNDERSTANDING:
Course aim is to provide the students with tools for the analysis of the performances and the evaluation of proper design solution for internal combustion engines and their core components. At the end of the course, the student will be able to independently understand the functional link between design variables and the performance of internal combustion engines also in case of innovative design,
APPLYING KNOWLEDGE AND UNDERSTANDING:
The course, through the analysis of specific problems and quantitative data, is aimed at providing the tools for analysis and evaluation of the effects of different design choices. The theme of energy efficiency and pollution reduction are at the heart of the teaching organization. The student will be able to interpret and propose design solutions, even innovative ones, adapted to the specificity of the problems that are presented to him.
MAKING JUDGEMENTS:
By studying theoretical and practical aspects of engine design and critically assessing the influence of different design variables, the student will be able to improve his judgment and proposal in relation to design. and the management of internal combustion engines.
COMMUNICATION SKILLS:
The presentation of the theoretical and application profiles underlying the operation of internal combustion engines will be carried out to allow the knowledge of the technical language of the appropriate specialist terminology; The development of communication skills, both oral and written will also be stimulated through classroom discussion, participation in seminary activities and through final tests.
LEARNING SKILLS:
The learning capacity, even individual, will be stimulated through numerical exercises, the drafting of papers on specialized topics, the discussion in the classroom, also aimed at verifying the actual understanding of the topics treated. The learning capacity will also be stimulated by integrative educational aids (journal articles and economic newspapers) in order to develop autonomous application capabilities.
SYLLABUS:
Legislation evolution on Internal Combustin Engines. Definition of the performance of the propulsion systems and their operating characteristics in relation to the mission, driving cycles. Generalities on reciprocating internal combustion engines: Characteristics and classification, thermodynamic and performance analysis of reciprocating internal combustion engines.
Air supply for 4-stroke engines: volumetric efficiency and its evaluation; Design elements of intake systems: quasi-stationary effects; valve sizing; influence of other engine parameters; Variable Valve Actuation systems. 2-stroke engines: construction schemes; Non-stationary phenomena in intake and exhaust ducts: inertia and wave propagation; variable geometry systems; calculation models; Supercharging.
In cylinder charge Motion: Turbulence; swirl, squish, tumble; stratified charge engines.
Traditional and alternative fuels; Properties of motor fuels. Generalities: combustibles; stoichiometric air; calorific value Gaseous fuels: natural gas, hydrogen and mixtures. bio-ethanol, bio-diesel and DME. Characteristics and their use in engines: technical solutions, performance and emissions.
Fuel supply Premixed combustion engines; Non-pre-mixed combustion engines.
Combustion : Analytical elements of combustion; thermodynamics of combustion processes; calculation of the chemical composition and of the adiabatic equilibrium temperature ; transport phenomena ; chemical kinetics.
Pollutant emissions and abatement systems; Emissions: formation mechanisms, effects on health and the environment, measurement of emissions; influence of engine parameters; Innovative combustion solutions, Advanced Thermodynamic Cycles. Sustainable mobility. Operating principles of hybrid vehicles: series and parallel solution; motors a.c. and electrical employees; regenerative braking; lithium batteries, performance and prospects. Plug-in hybrid vehicles, i.c. engines “range extender”. Innovative control logics for optimal powersplitting between the different energy sources. Electric vehicles, characteristics and prospects. Numerical simulation tools will be presented for all course topics
ATTENDANCE
Course attendance is strongly recommended. During the course, students are invited to interact with the Professor during the class or office hours for any clarification or insight in specific topics related to the program.
Digital Signal Processing – 6 CFU (optC1.b/optC2.b)
| 1 YEAR | II semester | 6 CFU |
| ICT and Internet Engineering | |
| Marina RUGGIERI (5cfu)
Tommaso ROSSI (1cfu) |
A.Y. 2023-24 A.Y. 2024-25 A.Y. 2025-26📑
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| Code: 8039514 SSD: ING-INF/03 |
The Digital Signal Processing teaching modules offer students the opportunity to become designers, providing a solid theoretical basis, multiple design techniques, and Matlab script development skills.
DSP is offered to Mechatronics students with the option of 6 credits and 9 credits format. Students who select the 6-credit option, might be interested to add a +3 credits of formative activities, with focus on pre-assigned additional topics of the DSP realm.
Thermodynamics and Heat Transfer (block A)
| 1 YEAR | II semester | 6 CFU |
| Michela GELFUSA | A.Y. 2021-22 (by Engineering Sciences)
A.Y. 2024-25 (last year) |
| Code: 80300063 SSD: ING-IND/10 (by Engineering Sciences) |
- Prerequisites: Knowledge of basic notions from physics courses, above all physical quantities, units of measurement, fundamental laws of mechanics, optics and electromagnetism.
- Objectives:
- LEARNING OUTCOMES: The course aims to provide students with the basic principles, physical laws, and applications of thermodynamics and heat transfer, with the dual purpose of preparing them to afford more applicative courses, and use the acquired knowledge for design and sizing simple components and thermal systems.
- KNOWLEDGE AND UNDERSTANDING: Students will have to understand the laws of applied thermodynamics and heat transfer, and understand the structure and operation of simplest components and systems. They will also demonstrate that they have acquired the basic methodologies for verifying and designing the studied devices.
- APPLYING KNOWLEDGE AND UNDERSTANDING: Students should be able to size and/or verify simple components and systems, such as, for example engine systems.
- MAKING JUDGEMENTS: Students will have to acquire the autonomous ability to face subsequent studies for which this course is preparatory, and to draw up simple projects of thermal systems that use the components studied. They will also have to be able to evaluate projects drawn up by other parties, checking that the project specifications are respected. COMMUNICATION SKILLS: Students must be able to illustrate in a complete and exhaustive way the acquired information, the results of their study and of their project activity, also through the normally used means of communication (discussion of the results obtained, report on the performed activity, Power Point presentations, etc.).
- LEARNING SKILLS: Students must be able to apply the physical laws underlying the studied phenomena, and to face further studies that use the acquired knowledge. They will have to be able to expand the already owned information through the analysis of technical-scientific literature, and to modify their curricula choosing future knowledge to be acquired on the base of their knowledge and tendency.
Mechanics of Materials and Structures – 6 CFU (block A-E)
| 1 YEAR | II semester | 6 CFU |
| Andrea Micheletti | A.Y. 2021-22 (9 cfu) |
| Andrea Micheletti | A.Y. 2022-23 A.Y. 2024-25 (6 cfu) – program 📑 |
| Code: 80300064 SSD: ICAR/08 (by Engineering Sciences) |
FORMATIVE OBJECTIVES
LEARNING OUTCOMES: The goal of this course, composed of two Modules, is to provide the student with basic knowledge of the mechanics of linearly elastic structures and of the strength of materials. By completing this class successfully, the student will be able to compute simple structural elements and reasonably complex structures.
KNOWLEDGE AND UNDERSTANDING: At the end of this course, the student will be able to:
– compute constraint reactions and internal actions in rigid-body systems and beams subjected to point/distributed forces and couples
– compute centroid position and central principal second-order moments of area distributions
– understand the formal structure of the theory of linear elasticity for beams and 3D bodies
– analyze strain and stress states in 3D bodies
– compute the stress state in beams subjected to uniaxial bending, biaxial bending, eccentric axial force
– understand the behaviour of beams subjected to shear with bending and torsion
– understand how to compute displacements/rotations in isostatic beam systems, how to solve statically underdetermined systems, how to apply yield criteria, and how to design beams against buckling
APPLYING KNOWLEDGE AND UNDERSTANDING: The student will apply the knowledge and understanding skills developed during the course to the analysis of practical problems. This includes the analysis of linearly elastic structures and structural members in terms of strength and stiffness.
MAKING JUDGEMENTS: The student will have to demonstrate his awareness of the modeling assumptions useful to describe and calculate structural elements, as well as his critical judgement on the static response of elastic structures under loads, in terms of stresses, strains, and displacements.
COMMUNICATION SKILLS: The student will demonstrate, mostly during the oral test, his capacity of analyzing and computing the static response of linearly elastic structures, as well as his knowledge of the underlying theoretical models.
LEARNING SKILLS: The student will get familiar with the modeling of structures and structural elements in practical problems, mostly during the development of his skills for the written test. This mainly concerns beams and three-dimensional bodies.
PREREQUISITES: The student should have already attended the basic courses of calculus, geometry, and physics.
It is required that the student has good skills with regard to differential and integral calculus, linear algebra and matrix calculations.
SYLLABUS:
Together with the other Module of this course, the following topics are covered.
Review of basic notions of vector and tensor algebra and calculus.
Kinematics and statics of rigid-body systems.
Geometry of area distributions.
Strain and stress in 3D continuous bodies and beam-like bodies.
Virtual power and virtual work equation for beams and 3D bodies.
One-dimensional beam models: Bernoulli-Navier model, Timoshenko model, constitutive equations, governing differential equations.
Constitutive equation for linearly elastic and isotropic bodies, material moduli.
Hypothesis in linear elasticity, equilibrium problem for linearly elastic beams and 3D bodies.
Three-dimensional beam model: the Saint-Venant problem, uniaxial and biaxial bending, eccentric axial force, shear and bending, torsion.
Elastic energy of beams and 3D bodies, work-energy theorem, Betti’s reciprocal theorem, Castigliano’s theorem.
Yield criteria (maximum normal stress, maximum tangential stress, maximum elastic energy, maximum distortion energy).
Buckling instability, bifurcation diagrams, load and geometry imperfections, Euler buckling load, design against buckling.
Basic notions on the finite element method and structural analysis software.