‹ Back to the directory

Biography

Philippe Martin is a researcher whose work focuses on automation, the control of dynamic systems, and real-time state estimation. His research covers, in particular, the estimation and control of electric machines (induction motors, PMSMs, SynRMs), with significant expertise in signal injection methods, the demodulation of multiplexed signals, and the analysis of multi-time-scale systems. His contributions include the development of nonlinear observers for estimating rotor speed, torque, or position, often under degraded conditions (low speed, absence of mechanical sensors). He also explores theoretical approaches such as high-order averaging analysis, singular perturbations, and Lyapunov methods to ensure the stability and robustness of the proposed solutions. His recent work addresses issues related to power converters, vibrating gyroscopes, and sensorless control, combining physical modeling and advanced mathematical tools for industrial and experimental applications.

Publication(s)

Teaching

Signal Processing

Lecturer

The Signal Processing course covers all aspects of harmonic analysis, including discrete- and continuous-time signals, convolution, operational calculus, and the Fourier transform. This content is presented in a way that makes it applicable in various contexts such as mathematics, engineering, and physics. The course establishes a fundamental link between the mathematical foundations of signal processing (Fourier analysis, wavelets, etc.) and the practical tools derived from them, such as filtering and compression. These concepts are illustrated by modern technologies that incorporate these principles, thereby providing a concrete and applied perspective on the theories covered. Furthermore, the course highlights the strong interconnections between signal processing and other fields covered by the Applied Mathematics course unit, naturally guiding instruction toward the fundamental mathematical concepts of the field. Practical exercises, designed to simply illustrate the theorems studied in class, reinforce this theoretical learning. Finally, the exploration of recent technological applications provides a current and dynamic perspective on the theoretical results presented. Independent study hours are devoted to projects on topics that go beyond the scope of the course. Recent projects have included the implementation of a music recognition algorithm based on the windowed Fourier transform, the study of signal processing tools used in tomography, and the application of signal processing tools to a geophysical problem.

Course I - Automation (PI MECATRO)

Course Director

PhD supervision

  • 2024 Anti-windup management for electric motor under electrical limitations WEHBE Ali
  • 2022 Algorithmic implementation of resonant gyroscopes MAROLLEAU Emilien
  • 2019 Sensorless electric motor drive by signal injection SURROOP Dilshad