M1 - Electronics, Electrical Energy, Automation - Robotics profile

The module will cover the following:

• Link transfer function and differential equation
• Continuous state representation and feedback (eigenvalues, stability)
• Representation and Sampled State Feedback
• Status feedback control without and with full loopback, LQR control
• State Observers
• Non-linear control with examples

Practical work: implementation of knowledge on real examples (e.g. electric motors), programming in python (numpy and control libraries).

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This course supplements basic training in signal processing with in-depth knowledge of deterministic or random digital signals. This knowledge is indispensable in all engineering sciences, as digital signal processing is currently used in the majority of applications.

The first part (10:30 h lecture, 6 h hands-on) covers the sampling and quantization of continuous signals, and the relationship between digital signals and the original continuous signal. We define the discrete Fourier transform of digital signals, its estimation and its use on real deterministic signals.

The second part of the course (9h Lecture, 4h30 TD, 3h TP) is dedicated to random signals and how the properties of certain random signals can be used either to reduce the random part of a signal whose deterministic part we wish to emphasize (filtering, increasing the signal-to-noise ratio, etc.) or to improve the transmission of information or identify complex linearized systems.

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• This teaching unit complements the basic training in analogue electronics with in-depth knowledge of signal filtering, amplification and modulation. This knowledge is essential for the understanding and realization of analog electronic systems in all fields of engineering sciences.
• Teaching is organized in the form of lectures, tutorials and practical work opening the possibility of mini-projects.

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This teaching unit, dedicated to the basics of digital electronics, is structured in an original way around a technical project, carried out individually or in pairs, the progress of which will follow the progress of the associated courses.

Each project topic will be assigned at the beginning of the teaching unit.

The main notions of digital electronics will be deepened through lectures and practical work may complement the theoretical aspects to guide the progress of the project.

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This teaching unit is made up of several parts, the first of which deals with the structures of the power electronics necessary to power an electronic system. The second will focus on the current or voltage regulation of these structures. A third part will focus on the conversion functions required to control MCC and DC Brushless actuators.

The last part presents actuator topologies for robotics and their implementation. The regulation of a DC motor and the self-pilot control of a synchronous motor will illustrate this last part.

Practical work will be carried out to observe the principle and implementation of regulated systems for electronics and actuators. This UE can be the support of the M1 project subjects.

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Computer engineering is the discipline that deals with the design, development and manufacture of computer systems, both from a hardware and software point of view.

This discipline has become fundamental in engineering sciences, whether in electronics, robotics, signal processing, measurement, etc. due to the important role that the computer has taken in all these areas.

This module aims to lead students to develop computer code in a volume corresponding to the scale of a complete software. The amount of code associated with it naturally creates a need to structure the code to keep it viable, and the concepts associated with structuring the code will therefore be addressed or reinforced.
Teaching is therefore mainly organised around practical work and projects. The context largely concerns deep themes of the EEA: signal processing (acquisition chain), instrument interfacing, and data transmission via the Internet on an embedded Linux platform. The topic of event-based programming through the development of graphical interfaces will also be addressed. The languages serving as support will be Labview and Python. Portions of C/C++ can be used at the initiative of the students in the projects.

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- Controller Summary.

- Robust synthesis and contingency management.

- Representation and synthesis of synchronous machines.

- Description/synthesis language.

- The basics of the VHDL language (entity, architecture, ...).

- Behavioral and structural descriptions.

- Simulation (Testbench).

- Reprogrammable circuits (CPLD, FPGA).

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TD courses in English for Specialization and English for Communication and which aims at professional autonomy in the English language.

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Project in partnership with a research laboratory and/or a company, highlighting the student's scientific skills, autonomy and adaptability.

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The internship or end-of-study project must highlight the student's scientific skills, autonomy and adaptability:

• Internship of 2 to 3 months (maximum 5 months) to be carried out in a research laboratory or within a company;
• or 3-month end-of-study project in a research laboratory or in a teaching project room.

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Description* :

1 - The aim is to allow students to understand the importance of a well-prepared application that is in line with an internship or job advertisement or in connection with the activities of a professional structure in the case of an unsolicited application; write CVs and cover letters; to know oneself better in terms of personality; use new technologies (social networks and job boards) and orient their research according to their professional project. Finally, to know how to prepare and behave during job interviews.

2 - It is a question of allowing students to write a scientific article following the completion of a project. To do this, they must know the objectives and characteristics, the plan to be applied, the different stages of implementation and the rules of presentation. Then, to present their project orally, students must know and be able to apply the general presentation structure; define appropriate and relevant visual aids; respect the rules of oral expression in order to express oneself correctly and in a professional manner (vocabulary, syntax, etc.); adopt behaviours that energize the discourse and allow you to hook your audience.

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Nowadays, image processing is omnipresent in information technologies: medicine, biology, agriculture, entertainment, culture, measurement, mechanics...

Image processing consists of applying mathematical transformations to images in order to modify their appearance or extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. Although it has long been carried out using electronic circuits, image processing is nowadays carried out almost exclusively digitally, i.e. via algorithms generally programmed with an imperative language (C, C++, Java, Python, etc.).

This teaching unit aims to give a solid foundation in image processing. It addresses, among other things, image formation and acquisition, colorimetric transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, etc. and an introduction to deep learning methods. The courses are complemented by support videos.

The teaching unit is mainly composed of 11 didactic courses covering the basics in the main areas of image processing and 3 practical work sessions whose subjects are to be chosen from 6 proposals. Students can choose to carry out the work on images they bring corresponding to their field of training.

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The module will cover the following:

• Introduction to the Git version control system
• Introduction to ROS Middleware for Robotics Applications
• Modularization of a robotic application

Practical work: Implementation of a ROS application, test on simulator and verification on real robot

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The module will cover the following:

• Introduction to robotics: history, type of robots, series and parallel mechanisms, applications
• Components (sensors and actuators)
• Trajectory generation (in the articular and operational spaces)
• Direct/Inverse Geometric Models, Direct/Inverse Kinematics Model
• Kinematic control and singularities
• Issues and applications in mobile robotics
• Non-holonomic models: unicycle, bicycle, car
• Sensors and odometry
• Distance finder and data fusion location (Kalman filter)
• Mapping (homogeneous transformations and KPIs)
• Navigation (installation regulation, path tracking)

Practical work: implementation of the skills on a real robot (either manipulator arm or wheeled robot), ROS programming with git and python.

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