Master in Electronics, electrical energy, automation

The aim of the course is to provide the basic mathematical tools to study continuous and discrete signals and linear time-invariant systems using SciPy.

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The principles of the wave propagation on transmission lines and the main characteristics are introduced in the framework of this course. Different transmission lines such as coaxial cables, or low-profile transmission lines (microstrip lines, coplanar waveguide) will be studied and circuits such as matching networks and filters will be discussed. The design and characterization of two-ports passive RF circuits will be explored in theory and in practical labs.

Content:

S parameters, ABCD, Y & Z matrices. Smith chart, matching networks. Signal-flow diagram. Classical low-profile transmission lines. Filters.

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In this module, we will lay the foundations for the control of linear systems in the continuous-time as well as in the discrete-time. In the continuous-time case, we will consider the time domain as well as the frequency domain. After a brief introduction, the following concepts will be addressed: transfer function, state-space representation of linear and nonlinear systems, linearization, Linear Time invariant (LTI) and Linear Time variant (LTV) systems, Input-Output stability.

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Lyapunov stability, controllability and observability, Gramians, poles and zeros of MIMO systems.

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Using a scientific programming language (e.g., Python) as a tool for modelling and numerical analysis.

 Outline:

1. Number representation systems and their precision
2. Data in Python
   1. Basic data structures: scalars, strings, lists, dictionaries, sets, tuples
   2. Matrix representations of numbers: the numpy ndarray (vs matrix),pandas data tables
   3. Read and write data according to the data type (CSV, JSON, pickle,. . . )
3. Array operations:
   1. Unitary operators MX0 –> MX1
   2. N-ary operators (MX0, . . . , MXn-1) –> MXn
4. Solving equations
   1. Linear matrix equations with applications to interpolation and regression
   2. Differential equations with applications to interpolation and prediction
5. Probability and statistics in Python
   1. Probability laws: distribution families, random variables, realisations
   2. Statistical tests

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This course offers an introduction to numerical analysis.

Content:

• Approximation and interpolation.
• Numerical integration and derivation.
• Numerical methods for nonlinear problems.
• Numerical resolution of ordinary differential equations (non-stiff, stiff) and differential algebraic equations.
• Numerical linear algebra:
  • Fast linear solvers (direct and iterative).
  • Fast eigen-solvers.

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Radio communications surround us and are nowadays at the core of many modern electronic applications. The syllabus of this course starts with the basics of analogical transmissions and goes up to the most advanced digital modulation techniques.

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Deterministic Linear Quadratic Regulator (LQR), Riccati equations, stochastic linear optimal control, Kalman filter, Linear Quadratic Gaussian (LQG) control, discrete-time linear optimal control and observers.

Objectives:

Solutions of optimal control and optimal state estimation problems with quadratic costs for deterministic and stochastic linear systems, in continuous and discrete-time.

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Numerical simulation methods of electrical circuits: generic techniques for setting equation of electrical circuits, numerical method for solving linear and nonlinear systems, iterative methods, solving differential equations.

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To know the basic concepts and basic assemblies of analog electronics.

Be familiar with the equivalent models of transistors in small signals.

1. General information on electrical circuits
2. Junction diode
3. Filed effect transistor
4. Differential amplifier
5. Operationnal amplifier

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The aim of this class is to provide technical skills for practical feedback control. Considering SISO systems, the analysis of sensitivity transfer functions and proper controller tuning are first considered. We then investigate the limitations of performances associated with the system properties and their impact on the feedback design. Finally, The modelling of uncertainties and the closed loop robustness are detailed.

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This course introduces students to scientific research methods.

Content

1. Bibliographic research
2. Scientific report writing
3. Oral communication and technical presentation.
4. Research and innovation strategy.

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Advanced embedded and robotic systems are more and more made of several computation units, spread out from the embedded object (Thing) to the cloud, and connected through communication links and network. Among other digital design considerations, processing performance, micro-controllers architecture, Internet of Things will be addressed in this course.

After completion of this course, student will be able to :

• apprehend the diversity in the micro-controllers current commercial offer,
• understand their internal differences and the consequences on computation performance, power consumption, cost...
• choose processing units suited to a specific application,
• program micro-controllers and cloud tools to control an embedded or IoT system.

Content

• Introduction to embedded systems,
• general internal architecture of micro-controllers for embedded systems,
• quick tour of dedicated internal structures to enhance processing performance, and speed up code execution : FPU, memory caching, DMA, SIMD,
• basics of the IoT : quick tour of communication links (Ethernet, LoRa…) and protocols (MQTT), Cloud database.

During the labs, focus will be set on :

• basics of micro-controller programming,
• performance measurement on two very different micro-controllers product lines,
• implement an IoT application, requiring cooperation of small autonomous robots, and central or cloud computation.

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Content

1. Maxwell in a vacuum
   • Maxwell's equations
   • Notion of distribution, charge and current distribution
   • Invariances and symmetries of the EM field
   • Interface and boundary conditions
   • Electrostatic case: Coulomb law, electric potential, conductors, dipoles
   • Magnetostatic case: Biot and Savart, magnetic potential, dipoles
   • Magnetodynamic case: induction phenomenon, induced currents
   • Wave case: propagation, reflection on a plane conductor, guided waves
   • Electromagnetic energy in vacuum
2. Maxwell in matter
   • Polarization of material
   • Microscopic origin of polarization
   • Macroscopic aspects of static polarization of dielectric materials
   • Polarization charges
   • Macroscopic fields in matter, dielectric susceptibility (tensor)
   • Microscopic origin of magnetization
   • Paramagnetism, diamagnetism
   • Macroscopic fields in matter, magnetic susceptibility (tensor)
   • Ferromagnetism: spontaneous magnetic order, domains, hysteresis cycles and magnetization processes
   • Electromagnetic energy in matter
3. Propagation of electromagnetic waves in materials
   • Reflection, transmission, absorption and dispersion

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This course is an introductory course on numerical field computation using the finite difference and the finite element methods.

Content:

• Finite difference method.
• Finite element method:
  • Strong and weak formulations.
  • Finite element analysis : domain discretization, local and global interpolation, numerical integration, assembly, resolution of linear system, etc.

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OSI model, Ethernet networks, TCP/IP, Network Address Translation

Course objectives: 

To be able to configure and manage a basic Local Area Network

Outline: 

- Topology, OSI model

- Ethernet Networks, CSMA/CD

- IP addressing and routing

- ARP - ICMP - Frame analysis

- TCP/UDP - Services - Network Address Translation

Labs : Building a simple LAN with Linux.

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Design using ADS Keysight software, and test using network analyzers, RF circuits: filters, power dividers, couplers and amplifiers.

Objectives: 

1. Know how to master a circuit and electromagnetic simulation tool in order to design RF circuits.
2. Understand the operation principle of elementary passive RF circuits: filters, couplers, power dividers.
3. Understand the operation principle of an elementary active RF circuit: low noise amplifier (LNA).

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An internship of period ranging from 2 months to 5 months shall be carried out in a company or in a scientific laboratory.

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Provide students with basic knowledge of the general principles of operation of electrical networks and the control of its elements. Future experts in electrical networks or engineers working on energy systems, this course will provide a system vision to students, with the notions of interactions and coupling of components.

LECTURE PART (10h)

General introduction (4hCM)

• General structure of electrical networks
• Costs and means of production
• New operating contexts (GED, deregulation, etc.)
• What are the key themes in this area?
• Links with the control part

Structure and modeling of alternators and typical grid components (2hCM)

• Modeling of alternators, presentation of simplified models
• Presentation of the different notions of settings (primary, secondary, tertiary)
• Adjustment means available (AVR, GOVER)
• Some basic regulators (PID, phase advance-delay, pole placement) - examples of AVR, GOVER
• Coupling of regulators

Structure and operation of electrical networks (1hCM)

• Power balance, notion of static balance, approach in the sense of the first harmonic
• Basic equations lines, concept of voltage drop and maximum power
• Planning elements

Load distribution calculation (1hCM)

• Presentation of the problem
• Load flow resolution method (backward, forward – Gauss-Seidel – Newton-Raphson)
• Illustration on a simple case

Concept of stability (2hCM)

• Rotor stability, equation
• Area criterion, application to a case of fugitive defect
• Metric frequency load shedding

PROJECT PART (40h)

The goal of this project is to apply the elements presented in the electrical network construction course as well as the notions of control and state representation.

The project begins with an introduction to a scientific tool for engineers (MATLAB). It continues with an application of the calculation of load distribution and the concepts of energy markets. A third axis corresponds to the synthesis of corrector applied to a single and composite corrector (Tubrine Gouvernor TG and Automatic Voltage Regulation AVR).

The final application system is an isolated network with several generators (equipped with their TG and AVR regulations). The alternators will be set for a given state of charge. Then several events will be applied in this network:

• Increase in load
• Loss of a work (line)
• Loss of an installation (primary and secondary)
• Application of a transient fault and effect on generator stability

Students will have to apply their knowledge to successfully stabilize the electrical network by adjusting the parameters of the correctors and setting up a suitable frequency-metric load shedding system.

Assessment: The grading policy comprises lab assessments plus a final examination. The grade of the module is the weighted average of the marks of each assessment.

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Provide students with basic knowledge of the general principles of operation of electrical networks and the control of its elements. Future experts in electrical networks or engineers working on energy systems, this course will provide a system vision to students, with the notions of interactions and coupling of components.

LECTURE PART (10h)

General introduction (4hCM)

• General structure of electrical networks
• Costs and means of production
• New operating contexts (GED, deregulation, etc.)
• What are the key themes in this area?
• Links with the control part

Structure and modeling of alternators and typical grid components (2hCM)

• Modeling of alternators, presentation of simplified models
• Presentation of the different notions of settings (primary, secondary, tertiary)
• Adjustment means available (AVR, GOVER)
• Some basic regulators (PID, phase advance-delay, pole placement) - examples of AVR, GOVER
• Coupling of regulators

Structure and operation of electrical networks (1hCM)

• Power balance, notion of static balance, approach in the sense of the first harmonic
• Basic equations lines, concept of voltage drop and maximum power
• Planning elements

Load distribution calculation (1hCM)

• Presentation of the problem
• Load flow resolution method (backward, forward – Gauss-Seidel – Newton-Raphson)
• Illustration on a simple case

Concept of stability (2hCM)

• Rotor stability, equation
• Area criterion, application to a case of fugitive defect
• Metric frequency load shedding

PROJECT PART (40h)

The goal of this project is to apply the elements presented in the electrical network construction course as well as the notions of control and state representation.

The project begins with an introduction to a scientific tool for engineers (MATLAB). It continues with an application of the calculation of load distribution and the concepts of energy markets. A third axis corresponds to the synthesis of corrector applied to a single and composite corrector (Tubrine Gouvernor TG and Automatic Voltage Regulation AVR).

The final application system is an isolated network with several generators (equipped with their TG and AVR regulations). The alternators will be set for a given state of charge. Then several events will be applied in this network:

• Increase in load
• Loss of a work (line)
• Loss of an installation (primary and secondary)
• Application of a transient fault and effect on generator stability

Students will have to apply their knowledge to successfully stabilize the electrical network by adjusting the parameters of the correctors and setting up a suitable frequency-metric load shedding system.

Assessment: The grading policy comprises lab assessments plus a final examination. The grade of the module is the weighted average of the marks of each assessment.

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 Many applications in the field of power and energy systems rely on optimal decisions making on many types of systems ranging from building, to microgrid, regional/national grid. The encountered case studies can be classified into two categories:

1. the operational planning of the systems – i.e. the management.
2. the long-term planning of the systems – i.e. the design/sizing of the system coupled with their management.

After an overview of the typical case studies, the module gives the students insights on how to translate them into optimization problems. A basic problem of unit commitment will be given in class as a running example (i.e. supply an electrical load from a set of generators with the minimum generation cost). Traducing the applications into an optimization problem requires to define sets of equation to account for the system objectives and operating limits. Specific constraints also need to be implemented in order to account for the model equations of the systems. Different families of formulations and solving algorithms will be introduced in class. Then three 4-hours labs will complete the module:

• Energy management strategy based on optimization for a microgrid – solar + storage
• Optimal design of a microgrid – solar + storage – under various assumption (Fig1.)
• Optimal operation of a simplified electrical grid.

Figure 1. Example of Results for the optima sizing of a microgrid (solar and storage sizes)

Students learn the methodical procedures that are necessary for successfully modeling and solving problems in the area of power and energy systems. They will learn how to handle Mathematical Programming Language with an example of package in Matlab. By the end of the course, students will be able to understand how any typical decision making applications can be translated into an optimization problem, beyond the scope of energy systems.

Assessment: The grading policy comprises lab assessments. The grade of the module is the weighted average of the marks of each assessment.

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This UE consists of two parts. One deals with modeling and simulation of electrical circuits, while the second covers approaches to system simulation.

• Numerical simulation methods of electrical circuits: generic techniques for setting equation of electrical circuits, graph theory, numerical method for solving linear and nonlinear systems, iterative methods, solving differential equations.
• System simulation and Artificial Intelligence

Assessment: The grading policy comprises homework and lab assessments plus a final examination. The grade of the module is the weighted average of the marks of each assessment.

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Optimal design aims at finding solution of complex design problems using optimization algorithms. The objectives of this course is to presents the fundamentals of an engineering design study, problem specification, modelling for design, optimization algorithms.

Assessment: The grading policy comprises homework and lab assessments plus a final examination. The grade of the module is the weighted average of the marks of each assessment.

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The aim of the course is to provide students with knowledge on the formulation of electromagnetic problems and their numerical solving using the finite element method. This course introduces the formulation of electromagnetic problems into mathematical boundary-value problems, the numerical discretization of continuous problems into discrete problems, and the development of rudimentary computer codes for simulation of electromagnetic fields in engineering problems, with aims of providing a general overview of the finite element method commonly used to model and simulate electromagnetic devices in electrical energy applications.

The main topics tackled are:

• Electromagnetic field models: electrostatics, electrokinetics, electrodynamics, magnetostatics, magnetodynamics and wave propagation,
• Electromagnetic field and potential formulations,
• Treatment of nonlinear materials (saturation, hysteresis) and permanent magnets,
• Computation of global quantities: lumped circuit elements (resistance, inductance, capacitance), flux linkage, Joule losses, iron losses,
• Coupling of electromagnetic field and circuit models,
• Computation of electromagnetic forces.

Particular attention is paid to state-of-the-art finite element techniques, modeling of problems and interpretation of numerical results. Practical work consists in simulating different electromagnetic problems by using the open-source mesh generator Gmsh (http://geuz.org/gmsh) and the own codes developed during the sessions.

Assessment: The grading policy comprises homework and lab assessments plus a final examination. The grade of the module is the weighted average of the marks of each assessment.

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Multiphysics and multiscale problems are more and more required for solving a wide range of engineering and physical problems. Especially those that are multidisciplinary in nature. A very typical area for this is electromechanics, where the coupled nature of the electrical, mechanical and thermal parts affects the operation of the driving mechanism, which is the case for e.g. electrical machines and piezoelectric devices. Another area is electrical components where electrical effects are strongly connected to heating effects and heat transfer for the cooling of the components. Moreover, the physical mechanisms act at different scales, in materials but also in the topologies of devices (e.g. multi-stranded coils).

Due to the coupling of physics and multiscale effects, it becomes more difficult to design devices from simple guidelines and formulas. This is where numerical methods are particularly efficient.

The module gives students insight into multiphysics modeling, i.e. on the integration of different physical phenomena into a computational model, and multiscale modeling. The main topics tackled are:

• Magneto-mechanical and magneto-thermal coupling solved by Finite Element Method,
• Integral equations method for thermal problems,
• Introduction to homogenization for electromagnetic problems.

Students learn the methodical procedures that are necessary for successfully solving modeling and simulation problems in the different areas of electrical engineering. The consolidation and deepening of the theoretical knowledge is achieved on the basis of specific problems that are solved with the appropriate methods and programs. By the end of the course, students will be able to know how to do build numerical models of physical problems, develop critical thinking in interpreting results from numerical analysis, and identify incorrect results. Moreover, validate experimental results against numerical modeling.

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The research project gives students the opportunity to familiarize themselves with the thematic area of their chosen field of specialization, as well as with the scientific methods of operation and the methodological approaches that are used in current research. This serves as a preparation for the Master’s thesis.

This takes place under the intensive supervision of an experienced researcher and generally in the context of research conducted by their research group. Working in a team gives students the opportunity to learn a good deal about the topics their colleagues are researching. The topic and tasks of the research projects are defined by the supervisor. The research project may also help the students to prepare for their Master's thesis.

Assessment: Each research project must be completed with a written report, to be submitted to the supervisor within 8 weeks or the specified time, and in any case before the start of the internship. The report should be prepared in the form of a scientific paper (title, abstract, introduction, results, discussion, materials and methods, literature; the scope of the project is usually 5 to 10 pages). The supervisor evaluates the written report on a "pass" or "fail" basis. Students with passing projects are awarded 3 ECTS. The supervisor is required to carry out a final project discussion with the student.

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• Project management: The objectives of this class are to supply the bases of the project management as well as to present the good practices in industries. The quality management according to the standards ISO and the piloting by process are presented through industrial projects. 

This class contains a method to establish a CV as well as simulations of real recruitment interview.

• Industrial and Research seminars: Invited keynote speakers give a short class on their research topic. The lectures (typically 15h) are given in the scope of the Master and in G2Elab laboratory. They focus on the latest results in a specific topic of dynamical systems, electromagnetic and multiphysics numerical modeling, and may include some labs to illustrate specific aspects.

The attendance is composed as Master and Ph.D students, as well as engineers, researchers and professors.

• Philosophical and ethical awareness seminars:
  • around the position and impact of digital/computing technology
  • around ethics in technology and science,
  • and other fields of philosophical/societal questioning around the role of science and scientific progress.

Assessment: The course requirements include the preparation of a poster according to the research theme of the invited speakers. The preparation of the poster will build on the seminars of the keynote invited speakers and a bibliography, which will be provided by the lecturers to the students, who will then have to analyze, interpret and present the main results of the developed methods. The final exam consists of a discussion given in the presence of the class.

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The second semester is devoted to a 5 to 6 months internship carried out on various topics and in different industries or research labs. This is a key step of this Master 2 curriculum and the transition towards a potential research program developed further during a PhD or a position in industry. This internship is also usually the student's first long-term experience with laboratory research. The student will perform research in his laboratory to reach the goals planned. This is a unique opportunity to test your practical and theoretical skills and choose a research topic.

It is worth noting that most students find internships in large industries, but that a significant number are enrolled in SMEs. This motivates the importance of entrepreneurial and research skills in addition to the classical industrial ones. The collaborative internships between academic research labs and industries contribute to the transfer of knowledge between university and industry and is thus strongly encouraged. The variety of industrial markets addressed appears to be a direct consequence of the need for electrical engineering in all high-tech processes with strong efficiency needs.

Strong motivation, solid scientific background will be the basis of the internship. The student must deal with the assigned topic and the hypothetical task independently and scientifically, including a comprehensive write-up of the topic and the implementation of the task as well as an oral presentation of the work.

The internship must be found during the first semester. The choice of internship must be approved by the Program Committee. Students must therefore have their choice of internship approved before end January.

Assessment: The work is assessed by means of a written Master's thesis to be submitted at the end of the period, an oral presentation and the assessment of the internship tutor. The language of the report and presentation will be French or English, as chosen by the student. Laboratory skills and scientific rigor are evaluated.

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• Public Speaking: Master students can choose to take an optional course in English, which is adapted to their level of English. However, since the minimum requirement for Master students is to be competent in giving a talk (with the aid of a visual support), to be able to take notes and summarise the contents of a lecture and to be able to read with ease, obligatory lessons are provided for those students who have not mastered these skills at the B2 level of the Common European Framework.

While the "rules" of public speaking are simple, actually integrating all the "good ideas" to the point at which they become one's own, in order to successfully transmit any information to any audience is not as simple as it might seem. The public speaking course is firmly anchored in the English as a Foreign Language (EFL) department - thus the transferable public speaking techniques, which are the main focus of the lessons, are in fact a pretext for improving the English of the participants. This follows the Dogme school of EFL, where the language work is based entirely on the "emergent language" of the participants.

Lessons contents

The 24 hours public speaking class, with a maximum number of 12 participants, provides the participants with a step-by-step hands-on experience of the importance of:

• using the visual channel to its full potential
• body language and eye contact
• the construction of the presentation
• markers and transitions
• pacing the talk
• the importance of rehearsing beforehand
• the impact of the conclusion
• tips and tricks to boost confidence
• handling questions from the audience

The integration of these techniques is reinforced by the observation of each other, of themselves - the participants are repetitively filmed for this purpose - and of professional speakers.

The students are invited to use the pretext of these classes to either review in depth some specific aspect of their instruction or on the contrary, to look into some subject more loosely connected with their main subject in order to inform and entertain their colleagues in the class.

• French as a Foreign Language: Master students can choose to take this optional course to learn, speak and write in French, which is adapted to their level of French. The objectives are to improve the student’s command of language and to learn to communicate in everyday life situations. This course also allows to discover French culture and to get familiar with the French way of life.

In this course, different aspects will be covered: grammar, phonetics, oral and writing comprehension exercises, notions of French culture. For the lessons, various supports are used: articles, schoolbooks texts, broadcasts from the Internet, short films, film extracts; and the activities include presentations, debates, writing, role-plays and lab works.

Assessment:

• French as a Foreign Language: Written or oral exam with duration.
• Public Speaking: The course requirements include the submission of a courselog, in which the student has recorded each week what they have individually learnt from the lesson, with a record of the new vocabulary encountered and how it is used. The final exam consists of a talk given in the presence of the class, the English teacher and a subject specialist teacher.

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 Optimization and Optimal Control (21 h + 15 h labs)

Multivariable robust control (20 h + 16 h labs)

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Feedback control design, diagnostic/supervision and process optimization typically require a specific modeling approach, which aims to capture the essential dynamics of the system while being computationally efficient. The first part of the class details the guiding principles that can be inferred from different physical domains and how multi-physics models can be obtained for complex dynamical systems while satisfying the principle of energy conservation. This leads to algebro-differential mathematical models that need to be computed with stability and computational efficiency constraints. System identification constitutes the second part of the class, to include knowledge inferred from experimental data in the input/output map set by the model. It provides methods to evaluate the model performance, to estimate parameters, to design "sufficiently informative" experiments and to build recursive algorithms for online estimation.

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The Adaptive Control course covers fundamental concepts and practical techniques to achieve or maintain a desired level of performance in a control system, especially when the parameters of the plant model are unknown or change over time.

Outline :

1. Introduction to the adaptive control

Basic Adaptive Control Configurations, Application examples.

2. Parameter adaptation algorithms

Gradient algorithm, recursive least squares Algorithm, stability of parameter adaptation algorithms.

3. Identification in open loop – a brief review

Data acquisition, model complexity, parameter estimation, model validation.

4. Iterative identification in closed loop and controller redesign

Algorithms for identification in closed loop (CLOE, F-CLOE and AF-CLOE), Validation of models identified in closed loop, Iterative identification in closed loop and controller re-design.

5. Direct and Indirect Adaptive Control

Tracking and regulation with independent objectives (known parameters), adaptive tracking and regulation with independent objectives (direct adaptive control), pole placement (known parameters), adaptive pole placement (indirect adaptive control).

Lab :

Iterative identification and controller re-design for the Throttle Valve.

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Embedded control & Labview (18 h)

• Become comfortable with the LabVIEW environment and data flow execution.
• LabVIEW Concepts: 
  • Acquiring, saving and loading data; 
  • Find and use math and complex analysis functions; 
  • Work with data types, such as arrays and clusters; 
  • Displaying and printing results. 
• Understand the principle of Embedded systems. 
• Be able to use Labview and the RT and FPGA Modules specially with Embedded systems. 
• Understand data exchanges between several systems.

Modeling labs (27 h)

Feedback control design, diagnostic/supervision and process optimization typically require a specific modeling approach, which aims to capture the essential dynamics of the system while being computationally efficient. The first part of the class details the guiding principles that can be inferred from different physical domains and how multi-physics models can be obtained for complex dynamical systems while satisfying the principle of energy conservation. This leads to algebro-differential mathematical models that need to be computed with stability and computational efficiency constraints. System identification constitutes the second part of the class, to include knowledge inferred from experimental data in the input/output map set by the model. It provides methods to evaluate the model performance, to estimate parameters, to design "sufficiently informative" experiments and to build recursive algorithms for online estimation.

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Safety, supervision and diagnosis of industrial plants 

The objective of this class is to introduce the concept of fault detection and fault diagnosis for complex systems and to present different classes of methods which have proven their performances in practical applications. 

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Security of Network and Applications (18h + 8h labs)

The objective of this class is to introduce security principles, on the theoretical, organizational and technical aspects. The points which are more specifically developed are: detection errors, firewall technics, network architecture, cryptology and VPN, anti-virus strategy. Are also discussed how to implement a security strategy, and some elements for the definition of a security policy. Some elements about safe networks, or networks for safety or critical applications, are also studied.

Field buses and Zigbee (10.5 h + 15h labs)

Distributed Algorithms and Network Systems (13.5 h + 6h labs)

Objectives Distributed algorithms aim at obtaining a global goal by exploiting a large number of simple devices (``agents''), and their local interactions. These algorithms can be for the purposes of estimation in a wireless sensor network, or control e.g. of a self-organized robotic fleet. This introductory class will first review the necessary tools from graph theory and Markov chains, and then present consensus: a prototypical example of distributed algorithm, as well as a building block for more complex algorithms. Theory will be accompanied by implementation on a real-world sensor network: FIT/IoT LAB.Class schedule 

1. Introduction: network systems 
2. Graphs: fundamentals of algebraic graph theory
3. Markov chains: convergence to invariant measure, Perron-Frobenius theorem 
4. Consensus (time-invariant graph) 
5. Consensus (gossip: randomly varying graph) 
6. Consensus-based algorithms: using consensus as a building block of other algorithms (e.g., localisation from relative measurements, least-squares regression, gradient descent minimization, distributed Kalman filter, counting nodes in an anonymous network) 
7. Labs (3): implementation of distributed algorithms on real sensor network, remotely using FIT/IoT LAB. Programming language: C.

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Under Floor Air Distribution for Intelligent Buildings

This new technology presents many advantages in comparison with traditional ventilation systems, such as energy consumption reduction, comfort and health. UFAD efficiency directly depends on distributed sensing capabilities (thanks to the deployment of a wireless sensor network) and on an appropriate multivariable feedback control design. The idea comes then to conceive a prototype in order to validate theoric and simulation results and to implement control algorithms. The prototype represents a ventilated floor composed of three interconnected levels: under floor, four rooms and upper floor. The related IPA projects are dedicated to air conditioning operation, with an emphasis on the modeling and control of airflow in each level and between the adjacent rooms. 

Controlling instability: the inverted half cube 

Unstable processes are typically not controllable with open-loop strategies and hence provide valuable benchmarks for feedback control applications. Addressing the stabilization of such processes implies a specific care of the key control design issues, such as performance limitations, communication and computation constraints, robustness, nonlinearities etc. 
The inverted half cube, designed and built by IPA students, implies to stabilize the half cube on its lower edge thanks to a cart driven with a LEGO NXT module. This novel version of the classical "inverted pendulum" implies to solve the same control problems as those associated with walking biped robots, a missile propelled by a jet reaction, a load suspended from a crane, etc...

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 Optimization and Optimal Control (21 h + 15 h labs)

Multivariable robust control (20 h + 16 h labs)

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Feedback control design, diagnostic/supervision and process optimization typically require a specific modeling approach, which aims to capture the essential dynamics of the system while being computationally efficient. The first part of the class details the guiding principles that can be inferred from different physical domains and how multi-physics models can be obtained for complex dynamical systems while satisfying the principle of energy conservation. This leads to algebro-differential mathematical models that need to be computed with stability and computational efficiency constraints. System identification constitutes the second part of the class, to include knowledge inferred from experimental data in the input/output map set by the model. It provides methods to evaluate the model performance, to estimate parameters, to design "sufficiently informative" experiments and to build recursive algorithms for online estimation.

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The Adaptive Control course covers fundamental concepts and practical techniques to achieve or maintain a desired level of performance in a control system, especially when the parameters of the plant model are unknown or change over time.

Outline :

1. Introduction to the adaptive control

Basic Adaptive Control Configurations, Application examples.

2. Parameter adaptation algorithms

Gradient algorithm, recursive least squares Algorithm, stability of parameter adaptation algorithms.

3. Identification in open loop – a brief review

Data acquisition, model complexity, parameter estimation, model validation.

4. Iterative identification in closed loop and controller redesign

Algorithms for identification in closed loop (CLOE, F-CLOE and AF-CLOE), Validation of models identified in closed loop, Iterative identification in closed loop and controller re-design.

5. Direct and Indirect Adaptive Control

Tracking and regulation with independent objectives (known parameters), adaptive tracking and regulation with independent objectives (direct adaptive control), pole placement (known parameters), adaptive pole placement (indirect adaptive control).

Lab :

Iterative identification and controller re-design for the Throttle Valve.

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Non linear control (20 h)

1. Introduction to nonlinear systems: representation and specific features 
2. Nonlinear systems analysis: stability, tangent linearization, Lyapunov methods 
3. State feedback control of nonlinear systems: approximate linearization, exact linearization, backstepping, sliding modes 
4. State observers for nonlinear systems: Extended Kalman Filter, Output injection, High gain designs 
5. Observer-controller schemes: adaptive methods, output feedback control 

 Predictive control (14 h)

• Predictive control 
  • Introduction to constraints
  • Finite horizon predictive control
  • Stability conditions
  • Examples
• Predictive control of nonlinear systems
  • Closed loop stability
  • Control parametrization
  • Optimization tools
  • Examples
• Complete case study

List of examples from Mechatronics: Inverted pendulum, tiliting trains, elastic crane, Boeing aircraft, chain of masses linked through springs, automate-manual transmission (AMT), etc.

Prerequisites: State space and transfer approaches for linear systems, optimisation

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Under Floor Air Distribution for Intelligent Buildings

This new technology presents many advantages in comparison with traditional ventilation systems, such as energy consumption reduction, comfort and health. UFAD efficiency directly depends on distributed sensing capabilities (thanks to the deployment of a wireless sensor network) and on an appropriate multivariable feedback control design. The idea comes then to conceive a prototype in order to validate theoric and simulation results and to implement control algorithms. The prototype represents a ventilated floor composed of three interconnected levels: under floor, four rooms and upper floor. The related IPA projects are dedicated to air conditioning operation, with an emphasis on the modeling and control of airflow in each level and between the adjacent rooms. 

Controlling instability: the inverted half cube 

Unstable processes are typically not controllable with open-loop strategies and hence provide valuable benchmarks for feedback control applications. Addressing the stabilization of such processes implies a specific care of the key control design issues, such as performance limitations, communication and computation constraints, robustness, nonlinearities etc. 
The inverted half cube, designed and built by IPA students, implies to stabilize the half cube on its lower edge thanks to a cart driven with a LEGO NXT module. This novel version of the classical "inverted pendulum" implies to solve the same control problems as those associated with walking biped robots, a missile propelled by a jet reaction, a load suspended from a crane, etc...

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The subject of this half-semester course are more advanced methods in convex optimization. It consists of 6 lectures, 2 x 1,5 hours each, and can be seen as continuation of the course “Non-smooth methods in convex optimization”.

This course deals with:

Evaluation : A two-hours written exam (E1) in December. For those who do not pass there will be another two-hours exam (E2) in session 2 in spring.

• Topic 1: convex analysis

• Topic 2: convex programming

• Basic notions: vector space, affine space, metric, topology, symmetry groups, linear and affine hulls, interior and closure, boundary, relative interior

• Convex sets: definition, invariance properties, polyhedral sets and polytopes, simplices, convex hull, inner and outer description, algebraic properties, separation, supporting hyperplanes, extreme and exposed points, recession cone, Carathéodory number, convex cones, conic hull

• Convex functions: level sets, support functions, sub-gradients, quasi-convex functions, self-concordant functions

• Duality: dual vector space, conic duality, polar set, Legendre transform

• Optimization problems: classification, convex programs, constraints, objective, feasibility, optimality, boundedness, duality

• Linear programming: Farkas lemma, alternative, duality, simplex method

• Algorithms: 1-dimensional minimization, Ellipsoid method, gradient descent methods, 2nd order methods

• Conic programming: barriers, Hessian metric, duality, interior-point methods, universal barriers, homogeneous cones, symmetric cones, semi-definite programming

• Relaxations: rank 1 relaxations for quadratically constrained quadratic programs, Nesterovs π/2 theorem, S-lemma, Dines theorem Polynomial optimization: matrix-valued polynomials in one variable, Toeplitz and Hankel matrices, moments, SOS relaxations

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This set of courses proposes an overview of recent techniques for the identification, observation, simulation and control of distributed parameter systems. This class of systems is widely used in physics and considered in many applications (such as in environment dynamics, airflow control, structural mechanics, and adaptive optics) having a large or an infinite number of degrees of freedom. A Partial Differential Equation (PDE) usually models them. Their mathematical study asks for a special care to analyze the dynamics behavior and to describe their control properties. Different aspects of this description are considered in this Teaching Unit, by emphasizing the practical methods allowing for some real applications. 

This Teaching Unit is composed by three different courses:

Analysis and control (13.5 h)

Modeling and Inverse problems (13.5h)

Distributed optimization  (13.5h)

Prerequisites: basic mathematical background, control theory of finite dimensional systems (control and observation theory for linear ODEs, in particular optimal LQ regulation) 

• Schedule 
• Modeling
• Control 
• Optimization 
• Communication networks
• Projects & seminars
• Public speaking 

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Embedded control & Labview (18 h)

• Become comfortable with the LabVIEW environment and data flow execution.
• LabVIEW Concepts: 
  • Acquiring, saving and loading data; 
  • Find and use math and complex analysis functions; 
  • Work with data types, such as arrays and clusters; 
  • Displaying and printing results. 
• Understand the principle of Embedded systems. 
• Be able to use Labview and the RT and FPGA Modules specially with Embedded systems. 
• Understand data exchanges between several systems.

Modeling labs (27 h)

Feedback control design, diagnostic/supervision and process optimization typically require a specific modeling approach, which aims to capture the essential dynamics of the system while being computationally efficient. The first part of the class details the guiding principles that can be inferred from different physical domains and how multi-physics models can be obtained for complex dynamical systems while satisfying the principle of energy conservation. This leads to algebro-differential mathematical models that need to be computed with stability and computational efficiency constraints. System identification constitutes the second part of the class, to include knowledge inferred from experimental data in the input/output map set by the model. It provides methods to evaluate the model performance, to estimate parameters, to design "sufficiently informative" experiments and to build recursive algorithms for online estimation.

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Safety, supervision and diagnosis of industrial plants 

The objective of this class is to introduce the concept of fault detection and fault diagnosis for complex systems and to present different classes of methods which have proven their performances in practical applications. 

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Project management (10.5 h)

The objectives of this class are to supply the bases of the project management as well as to present the good practices in industries. The quality management according to the standards ISO and the piloting by process are presented through industrial projects. 

This class contains a method to establish a CV as well as simulations of real recruitment interview.Class schedule 

Industrial seminars (27 h for IPA)

• Semi-active damper design and regulation. B. TALON, SOBEN.
• Model-based design for motor control. V. TALON, Renault.
• Case study on warm compression station for cryogenics. B. BRADU, CERN . 
• Keys and social issues to enter the industrial life. M. PRUNIER, Schneider.
• Consulting for inovation in Information Technologies. D. JACQUET, Protoptim .

Research seminars (15 h for CST)

Each year, MiSCIT and GIPSA-lab invite keynote speakers to give a short class on their research topic. The lectures (typically 15h) are given in the Control Systems Department of GIPSA-lab. They focus on the latest results in a specific topic of control and systems theory, and may include some labs to illustrate specific aspects. The attendance is composed as Master and Ph.D. students, as well as engineers, researchers and professors. A basic knowledge in dynamical systems, linear algebra and control theory is expected.

Linear Matrix Inequalities and Sum-of-Squares Optimization in Systems and Controls Theory: A Practical and Theoretical Overview (2013) 

By Mattew M. Peet, Professor of Aerospace Engineering, Arizona State University (USA)

Abstract: The topic of this course will be the use of LMI methods for optimal control of linear, nonlinear and infinite-dimensional systems. We will start by posing all major finite-dimensional optimal control problems as LMIs. This includes both output and full-state feedback control for both the H_\infty and H_2 (LQG) system norms. We will also give a brief introduction to the popular SDP solver SeDuMi. Next, we will give a background on the use of LMIs for optimization of polynomial variables such as in the Sum-of-Squares framework - including the use of the Matlab toolbox SOSTOOLS. We will discuss several theoretical tools for the optimization of polynomials such as various versions of the Positivstellensatz. Finally, we will discuss how LMIs and polynomial optimization have been use to resolve long-standing problems in analysis of nonlinear systems and systems with delay, and how these results have been extended to synthesize optimal controllers for systems with delay and certain classes of partial-differential equations.

Syllabus:

• Day 1: Convex optimization; Semidefinite programming; Linear state-space systems theory; Optimal H_2 and H_\infty dynamic output-feedback controller synthesis. 
• Day 2: Polynomial optimization, Sum-of-Squares; Polya's lemma; Ideals, Varieties; The Positivstellensatz; Robust controller synthesis 
• Day 3: Nonlinear stability analysis; Analysis and control of linear delayed systems; Analysis and control of linear partial-differential equations; Stability Analysis of nonlinear delayed and partial-differential equations. 

Model Reduction (Approximation) of Large-Scale Systems (2012) 

By Charles Poussot-Vassal, Researcher at Onera - The French Aerospace Lab.

Abstract: In the engineering area (e.g. aerospace, automotive, biology, circuits), dynamical systems are the basic framework used for modelling, controlling and analysing a large variety of systems and phenomena. Due to the increasing use of dedicated computer-based modelling design software, numerical simulation turns to be more and more used to simulate a complex system or phenomenon and shorten both development time and cost. However, the need of an enhanced model accuracy inevitably leads to an increasing number of variables and resources to manage at the price of a high numerical cost. This counterpart is the justification for model reduction (see Figure 1). 
The objective of the lecture is to introduce the model reduction (or approximation) problem, within the linear framework only, and, in an increase complexity, some of the well established and modern techniques to solve this class of problem. The lecture is also coupled with two Matlab-based labs, in order to emphasize the numerical difficulties and to provide the participant an insight of the existing tools.

Syllabus:

• Day 1: Introduction, motivating examples and model reduction problem; Overview of the approximation methods and linear algebra tools; Gramian and SVD based techniques; Moment matching and Krylov subspace based techniques.
• Day 2: Lab 1, Application of the SVD techniques and the Arnoldi procedure; H2 first order optimality conditions, generalized Krylov subspace and Tangential techniques; Advanced techniques (Mixed and Sylvester approaches / Multi-LTI and LPV problems / Tools).
• Day 3: Lab 2, Krylov based techniques and MORE Toolbox (developed within Onera by C. Poussot-Vassal).

Design Projects: analysis (15 h)

Under Floor Air Distribution for Intelligent Buildings

This new technology presents many advantages in comparison with traditional ventilation systems, such as energy consumption reduction, comfort and health. UFAD efficiency directly depends on distributed sensing capabilities (thanks to the deployment of a wireless sensor network) and on an appropriate multivariable feedback control design. The idea comes then to conceive a prototype in order to validate theoric and simulation results and to implement control algorithms. The prototype represents a ventilated floor composed of three interconnected levels: under floor, four rooms and upper floor. The related IPA projects are dedicated to air conditioning operation, with an emphasis on the modeling and control of airflow in each level and between the adjacent rooms. Controlling instability: the inverted half cube 

Unstable processes are typically not controllable with open-loop strategies and hence provide valuable benchmarks for feedback control applications. Addressing the stabilization of such processes implies a specific care of the key control design issues, such as performance limitations, communication and computation constraints, robustness, nonlinearities etc. 
The inverted half cube, designed and built by IPA students, implies to stabilize the half cube on its lower edge thanks to a cart driven with a LEGO NXT module. This novel version of the classical "inverted pendulum" implies to solve the same control problems as those associated with walking biped robots, a missile propelled by a jet reaction, a load suspended from a crane, etc...

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The objective of this course is to provide a comprehensive foundation in system reliability theory and dependability analysis methods. The following topics are addressed.

• Probabilistic failure models and lifetime modelling of engineering components
• Qualitative approaches to system reliability analysis (FMECA, ...)
• Reliability modelling and analysis of systems and networks of independent components (Fault Tree Analysis, Reliability Block Diagrams, Event Tree Analysis, Structure Function, Minimal Cutsets, Importance Measures)
• Markov processes for systems and networks reliability modelling (systems with dependent components, passive redundancies, tested components, ...)
• Reliability of maintained systems and maintenance policies modelling

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Project management (10.5 h)

The objectives of this class are to supply the bases of the project management as well as to present the good practices in industries. The quality management according to the standards ISO and the piloting by process are presented through industrial projects. 

This class contains a method to establish a CV as well as simulations of real recruitment interview.Class schedule 

Industrial seminars (27 h for IPA)

• Semi-active damper design and regulation. B. TALON, SOBEN.
• Model-based design for motor control. V. TALON, Renault.
• Case study on warm compression station for cryogenics. B. BRADU, CERN . 
• Keys and social issues to enter the industrial life. M. PRUNIER, Schneider.
• Consulting for inovation in Information Technologies. D. JACQUET, Protoptim .

Research seminars (15 h for CST)

Each year, MiSCIT and GIPSA-lab invite keynote speakers to give a short class on their research topic. The lectures (typically 15h) are given in the Control Systems Department of GIPSA-lab. They focus on the latest results in a specific topic of control and systems theory, and may include some labs to illustrate specific aspects. The attendance is composed as Master and Ph.D. students, as well as engineers, researchers and professors. A basic knowledge in dynamical systems, linear algebra and control theory is expected.

Linear Matrix Inequalities and Sum-of-Squares Optimization in Systems and Controls Theory: A Practical and Theoretical Overview (2013) 

By Mattew M. Peet, Professor of Aerospace Engineering, Arizona State University (USA)

Abstract: The topic of this course will be the use of LMI methods for optimal control of linear, nonlinear and infinite-dimensional systems. We will start by posing all major finite-dimensional optimal control problems as LMIs. This includes both output and full-state feedback control for both the H_\infty and H_2 (LQG) system norms. We will also give a brief introduction to the popular SDP solver SeDuMi. Next, we will give a background on the use of LMIs for optimization of polynomial variables such as in the Sum-of-Squares framework - including the use of the Matlab toolbox SOSTOOLS. We will discuss several theoretical tools for the optimization of polynomials such as various versions of the Positivstellensatz. Finally, we will discuss how LMIs and polynomial optimization have been use to resolve long-standing problems in analysis of nonlinear systems and systems with delay, and how these results have been extended to synthesize optimal controllers for systems with delay and certain classes of partial-differential equations.

Syllabus:

• Day 1: Convex optimization; Semidefinite programming; Linear state-space systems theory; Optimal H_2 and H_\infty dynamic output-feedback controller synthesis. 
• Day 2: Polynomial optimization, Sum-of-Squares; Polya's lemma; Ideals, Varieties; The Positivstellensatz; Robust controller synthesis 
• Day 3: Nonlinear stability analysis; Analysis and control of linear delayed systems; Analysis and control of linear partial-differential equations; Stability Analysis of nonlinear delayed and partial-differential equations. 

Model Reduction (Approximation) of Large-Scale Systems (2012) 

By Charles Poussot-Vassal, Researcher at Onera - The French Aerospace Lab.

Abstract: In the engineering area (e.g. aerospace, automotive, biology, circuits), dynamical systems are the basic framework used for modelling, controlling and analysing a large variety of systems and phenomena. Due to the increasing use of dedicated computer-based modelling design software, numerical simulation turns to be more and more used to simulate a complex system or phenomenon and shorten both development time and cost. However, the need of an enhanced model accuracy inevitably leads to an increasing number of variables and resources to manage at the price of a high numerical cost. This counterpart is the justification for model reduction (see Figure 1). 
The objective of the lecture is to introduce the model reduction (or approximation) problem, within the linear framework only, and, in an increase complexity, some of the well established and modern techniques to solve this class of problem. The lecture is also coupled with two Matlab-based labs, in order to emphasize the numerical difficulties and to provide the participant an insight of the existing tools.

Syllabus:

• Day 1: Introduction, motivating examples and model reduction problem; Overview of the approximation methods and linear algebra tools; Gramian and SVD based techniques; Moment matching and Krylov subspace based techniques.
• Day 2: Lab 1, Application of the SVD techniques and the Arnoldi procedure; H2 first order optimality conditions, generalized Krylov subspace and Tangential techniques; Advanced techniques (Mixed and Sylvester approaches / Multi-LTI and LPV problems / Tools).
• Day 3: Lab 2, Krylov based techniques and MORE Toolbox (developed within Onera by C. Poussot-Vassal).

Design Projects: analysis (15 h)

Under Floor Air Distribution for Intelligent Buildings

This new technology presents many advantages in comparison with traditional ventilation systems, such as energy consumption reduction, comfort and health. UFAD efficiency directly depends on distributed sensing capabilities (thanks to the deployment of a wireless sensor network) and on an appropriate multivariable feedback control design. The idea comes then to conceive a prototype in order to validate theoric and simulation results and to implement control algorithms. The prototype represents a ventilated floor composed of three interconnected levels: under floor, four rooms and upper floor. The related IPA projects are dedicated to air conditioning operation, with an emphasis on the modeling and control of airflow in each level and between the adjacent rooms. Controlling instability: the inverted half cube 

Unstable processes are typically not controllable with open-loop strategies and hence provide valuable benchmarks for feedback control applications. Addressing the stabilization of such processes implies a specific care of the key control design issues, such as performance limitations, communication and computation constraints, robustness, nonlinearities etc. 
The inverted half cube, designed and built by IPA students, implies to stabilize the half cube on its lower edge thanks to a cart driven with a LEGO NXT module. This novel version of the classical "inverted pendulum" implies to solve the same control problems as those associated with walking biped robots, a missile propelled by a jet reaction, a load suspended from a crane, etc...

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This course is supported by the "College Doctoral" of Grenoble University. It is given in English upon request at the beginning of the session.

Summary:

Data assimilation is often presented as the art of combining various sources of information (most often, measurements and numerical models) to estimate the state of a partially observed dynamical system. In geophysics, it is now a research topic per se. It is mainly used to:
    •    define as precisely as possible a physical state (atmosphere, ocean, ...) of a system to predict its temporal evolution;
    •    optimally estimate a system state over a period of time for example, to study its variabilities;
    •    identify systematic errors in models;
    •    optimize the design of observation networks;
    •    extrapolate values of non observed variables;
    •    estimate parameters in physical laws.
The course aims at introducing the theoretical concepts and practical implementation aspects of modern data assimilation with a peculiar focus on high dimensional, non linear systems, as usually met in geosciences.

Necessary background for the course:

- Basic notions in probability and statistics (Expectation, variance, covariance matrix)
- Basic notions in linear algebra
- Basic notions in differential calculus

Program:

Part 1: Data assimilation based on estimation theory

1. Introduction to ensemble data assimilation
2. Notions in estimation theory
3. The BLUE
4. The Kalman filter
5. Ensemble Kalman filters
6. Non linear filters

Part 2: Data assimilation based on control theory

1. Introduction to variational data assimilation
2. Variational data assimilation for time-independent problems
3. The adjoint method
4. Variational Data assimilation : Practical aspect
5. Adjoint coding

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The goal of this teaching module is to understand the digital modulation techniques used in radio communication systems and high data rate wireline systems, and to learn the design knowledge of analog and mixed integrated functions for signal processing applied to radio communications. Radiocommunication system design will be practically illustrated during labwork sessions. 

This teaching module will be divided into 4 parts

- Wireless communications – 18 hours – 2.5 ECTS

Understanding of the modulation techniques for fixed (WiFi, UWB, Zigbee, DVB...) and mobile (2G, 3G, 4G) radio communication systems. Impact of the radio channel on digitally modulated signals is introduced, advanced modulations formats and techniques are described, impact of the different RF front-end components on the digitally modulated waveforms is discussed.

- Analog and Mixed Systems for signal processing – 20 hours – 2.5 ECTS

Design knowledge of analog and mixed integrated functions for signal processing applied to radiocommunications. Optimal filtering in an RF analog receiver chain before conversion. Continuous time analog filter (GmC). Switched capacitor filter. Fully differential integrated Operators (Amplifiers). Oversampling converters Sigma Delta

- High data rate wireline systems – 8 hours – 1 ECTS

Introduction to high data rate wireline communication systems and short range interconnections. Basics on baseband digital modulations. Interconnection systems. Silicon based integrated technologies for interconnections

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The goal of this teaching is to acquire a good understanding of:

• Analog RF integrated circuit design,
• Analog Signal Processing in RF,
• Basic concepts in RF design.
• RF Front End Architectures for integration.
• Technology and modeling of integrated devices for RF.
• Design principles of basic RF blocks (LNA, Mixers, VCO, Power Amplifiers).

This teaching module will be divided into 2 parts

- Radiofrequency Integrated Circuits (course) – 14 hours – 3 ECTS

-  Lab work: Design of integrated RF circuits – 24 hours – 3 ECTS

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The goal of this course is to explore the theory, design and characterization techniques of the main passive circuits appearing in wireless communication systems: power dividers, matching networks, couplers, baluns, filters, …
Only passive circuits based on distributed approach (transmission lines) will be addressed, in PCB, CMOS/BiCMOS and alternative technologies, from RF to mm-wave circuits. The circuits are based on classical transmission lines like microstrip, coplanar or SIW (Substrate Integrated Waveguide), but a large focus will be done on new transmission lines based on slow-wave concepts, including slow-wave CPW, slow-wave microstrip and slow-wave SIW.
The design of tunable passive circuits will also be discussed.
The characterization techniques will be explored in theory and in practical labs.

Content: S parameters, ABCD, Y & Z matrices. Smith chart, matching networks. Signal-flow diagram. Classical low-profile transmission lines: microstrip, coplanar (CPW & CPS). Substrate integrated waveguides (SIW). Slow-wave structures. Design of power dividers, matching networks, couplers, baluns, filters, phase shifters. Characterization and de-embedding techniques.

This teaching module will be divided into 2 parts

- Microwave passive circuits (course) – 24 hours – 3 ECTS

-  Lab work: Design and characterization of microwave passive circuits – 24 hours – 3 ECTS

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The goal of this teaching module is to understand the electromagnetic radiation mecanisms.

This teaching module will be divided into 2 parts

- Antennas – 16 hours – 2 ECTS

The aim is to understand how to design and characterize antennas. The following contents will be studied:

• Radiation of a dipole 
• Main antenna parameters: radiation pattern, directivity, ...,
• Antennas such as microstrip patch, Antennas network, beamsteering/beamforming antenna array

- Signal integrity – 10 hours – 1 ECTS

Basic principles will be explained such as:

• Digital signals: spectral distribution of signals and knee frequency, rise time, overshoot, ringing, crosstalk
• Transmission lines modelling
• Coupling, crosstalk (amplitude, delay), SSN, substrate coupling
• Packaging (processes, single and hybride dies, influence of the package)

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The goal of this course is to give a general view on the fabrication processes that exist in microelectronics.

This teaching module will be divided into 2 parts

- Standard and alternative microelectronics technologies – 20 hours – 2 ECTS

Focus is made on integrated silicon applications. Various standard technologies will be presented in CMOS bulk, BiCMOS, FD SOI, interposers. Special attention will be paid on RF and millimeter waves constraints. Weighing between the pros and cons will enable to enface a specific technology for a specific application, digital and/or analog, RF and/or millimeter waves. An overview of potentially future trends will be drawn also with alternative technologies: MEMS vs varactors for tunability, graphene for very high mobility channels.

- Clean room based fabrication – 8 hours – 1 ECTS

An 8-hour tutorial in the cleanroom of the CIME-Nanotech will illustrate this class.It will be dedicated to the clean-room presentation and the fabrication of diodes or MOM capacitors.

In the framework of these courses, the following topics will be presented: Fabrication process in clean-room. From sand to silicon wafer. Cleaning techniques. Material deposition: epitaxy, sputtering, chemical vapor deposition. Material transformation: wet and dry oxidation. Doping: diffusion, ionic implantation. Lithography. Chemical etching,  physical etching, chemical mechanical polishing. Standard technologies front-end and back-end, CMOS for digitals and low-frequencies, FD SOI for low consumption, BiCMOS for high frequencies and millimeter waves analogs, silicon interposers for taking advantage of various technologies. Specific constraints for RF and millimeter waves consideration: dummies, coupling, back-end thickness. Alternative technologies: MEMS vs varactors. Alternative technologies: graphene and high mobility channels

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In the framework of the Research Lab work, the students are integrated in a research laboratory one-day-per-week (in the first semester of Master WICS). Supervised by a researcher, they work on a research topic to acquire a first experience of research world.

At the end of this Lab work, a publication writing based on the obtained results must be done by taking into account the standard scientific publication rules (title, authors, abstract, introduction, content with several sections (i.e. Theory, Simulation…), conclusion, and references).

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This teaching module will be divided into 4 parts :

- Design for test - 8 hours - 0.75 ECTS

This teaching module provides a comprehensive description of test methods for RF ICs.Content: Introduction to RF production testing (What is test? Characterization vs. verification vs. production testing; IC testing methodology, objectives, challenges; Modeling the cost of test); Current industrial practice for RF testing (Testing RF stand-alone ICs: basic measurements such as power, noise, gain, linearity, S-parameters for low noise amplifiers, mixers, power amplifiers, etc; Testing RF transceivers: receiver and transmitter tests); Advanced test techniques (Design for Test (DfT) solutions; Built-In Self-Test (BIST) solutions; Practical case studies: loopback test, RF power detectors, envelope detectors, current sensors, non-intrusive process monitors, temperature sensors, etc); State-of-the-art research papers reading and discussion.

- Radio Frequency IDentification Technologie - 8 hours - 0.75 ECTS

This teaching module allows understanding the problematic of the integration of sensors in RFID systems : use and implementation of a UHF RFID system; Realization of a RFID system from basic electronic bricks; Design and characterization of a RFID tag.

Content: Introduction, history and use case of the RFID technology; Basics of RFID; RF concepts of UHF RFID : Radar cross section, backscattering wave, load modulation; UHF RFID reader : modulation, transceiver, smart antenna; UHF RFID tag : design, energy harvesting, sensor; UHF RFID protocol : communication protocol between the reader and the tag; The future of RFID.

- Electrooptic sensors & Bio electromagnetism - 8 hours - 0.75 ECTS

The aim of this module is to give an overview of the existing techniques related to the electric field analysis within harsh environments. More precisely, this course will explain both the fundamentals of optical modulations in non-linear crystals and how to exploit this phenomena to develop suitable sensors. A focus will be dedicated to the influence of the ambient media on the sensor behavior. Examples of measurements in biological media and in ionized media with specific electro-optic sensors will be given.

Content: Introduction and context: why measuring the electric (E) field ? requirements for E-field measurement; definition of relevant characteristics for E-field sensors; benchmarking of exiting sensors; Optical sensor for the E-field vectorial analysis; Active sensors : principles & perfomances; Example of field assessment; Passive sensors: principles & perfomances; Example of field assessment in air, in biological media; Example of intense field assessment associated to partial or total discharge; Example of intense field assessment in the vicinity of plasmas for biomedical applications.

- Tunable RF - Technologies & Applications - 8 hours
- 0.75 ECTS

The need for wireless devices with improved battery life, smaller form factor, and reduced cost is driving research towards flexible RF Front-End Modules (FEM) with a high integration level to efficiently afford multimode multi-band requirements. Today’s RF FEM are mostly addressed by multiple modules using a large set of technologies which lead to large and costly solutions. As the push for further cost and size reduction goes on, many research activities and industrial efforts are currently on going to improve RF FEM integration level by introducing new technologies and design techniques. The proposed teaching module will provide an overview of technologies and advanced design techniques for the implementation of high performance tunable RF building blocks and RF FEM.

Content: Design and analysis of reconfigurable RF circuits (PA, Filter,…) and RF FEM.  Topics Include: RF FEM and PA architectures; Technologies for tunable RF design; Tunable PA architecture and design; Tunable RF Filter architecture and design; Tunable matching design; Antenna tuning.

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Five to six-months research internship in a laboratory, an in-firm R&D center or in a scientific research organization.

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In the framework of the Research Lab work, the students are integrated in a research laboratory one-day-per-week (in the first semester of Master WICS). Supervised by a researcher, they work on a research topic to acquire a first experience of research world.

At the end of this Lab work, an oral presentation based on the obtained results must be done by taking into account the scientific communication rules in international conferences (1 slide per minute (not more), title, authors, context, brief state-of-the-art, scientific approach..., theory, simulation…, conclusion and prospects).

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Justify or obtain level B2 (oral comprehension, oral expression, written proficiency) in a foreign language. During this module, students on work-linked training will also develop their ability to take notes and give oral presentations in English.

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