| Course label : | Real time estimation for engineers |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister WILFRID PERRUQUETTI |
| Education language : | French |
| Potential ects : | 4 |
| Results grid : | |
| Code and label (hp) : | G1G2_ED_EEA_RTE - Real time estimat.engineers |
Education team
Teachers : Mister WILFRID PERRUQUETTI
External contributors (business, research, secondary education): various temporary teachers
Summary
Educational goals
At the end of the course, the student will be able to: - understand, - analyze, - and develop solutions, for various estimation problems. These estimation problems concern: identifiability and identification of uncertain parameters in the system equations, including delays, (linear or nonlinear and even for closed loop systems); estimation of state variables, which are not measured (even for closed loop systems); fault diagnosis and isolation; observer-based chaotic synchronization, localizability of mobile robots (including drones, wheeled mobile robots, and underwater vehicles), estimation of time derivative for noisy signal (with some applications in signal processing).
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Students will be evaluated on the basis of a project (case study from the worlds of robotics, living systems...). The projects will be presented at the beginning of the elective during the introductory session
Online resources
Some have to be developped - Pdf for each master session and practical session, - Matlab/Simulink code (complete solution or partial one depending on the context), - external web links (using Wikipedia & <https://fr.mathworks.com> webinar & online solution and courses)
Pedagogy
Project and case study. The plan giving the learning sequence is given below: I Introduction II Linear/non linear regression (introduction to satistic model) II Linear Model Based Technics 1. Observability, Identifiability, localizability (Robotics), ￜ 2. Geometric framework 3. Algebraic framework 4. Linear design (Kalman/Luenberger observers, full/uncomplete estimatorￜ) 5. To work or not to work with a linearized system ? III Non Linear Model Based Technics 1. Introduction to non linear problems (Observability, Identifiability, localizability, ￜ) 2. Geometric framework 3. Algebraic framework 4. Uniform observability & Local decomposition 5. Non linear estimator design (High gain, Homogeneous, Sliding Mode) IV Ultra-local Model Based Technics (or Model free technics) 1. Introduction 2. Algebraic Annihilators 3. Parameters estimation 4. Real-time Derivative estimation 5. State estimation
Sequencing / learning methods
| Number of hours - Lectures : | 16 |
|---|---|
| Number of hours - Tutorial : | 18 |
| Number of hours - Practical work : | 14 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 48 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Some basic linear control technics (linear state feedback), basic mathematics (linear algebra, basic algebra (such as ring, group) and basic analysis (differentiation, ￜ) and some basic physics (electrical laws and mechanics)
Maximum number of registrants
64