M*****N
About Candidate
I have a solid background in Electrical Engineering with hands-on experience in the design and analysis of control systems, particularly within the context of Electric Vehicles. My academic and internship projects have focused on the observation and control of nonlinear systems, where I applied advanced theoretical concepts to practical engineering problems. In addition, I am proficient in Python, C++, and MATLAB—skills I have utilized extensively for simulation, algorithm development, and system modeling.
Salary
Nationality
Looking for Job Title
Iqama
Location
Education
I hold a Master’s degree in Electrical Engineering and Control Systems from Université Grenoble Alpes (UGA), France, where I was awarded a prestigious scholarship in recognition of my academic potential. During my studies, I consistently ranked among the top students in my class, demonstrating strong analytical skills, technical proficiency, and a deep understanding of advanced control theory and electrical system design.
Work & Experience
I am working on the observation of nonlinear behavior in control systems, focusing on designing robust state estimators for systems affected by uncertainties and nonlinear dynamics. My work primarily involves the implementation and comparison of Extended Kalman Filters (EKF) and Zonotopic Mean Value Filters (ZMVs). The Kalman Filter is widely used for linear systems, but for nonlinear systems, I apply the Extended Kalman Filter, which linearizes the system dynamics around the current estimate. While EKF is effective, its performance may degrade in the presence of high nonlinearities or bounded uncertainties. To address these limitations, I am also implementing the Zonotopic Mean Value Filter, which is particularly well-suited for systems with bounded noise and uncertainties. It uses set-membership estimation techniques to compute state bounds instead of point estimates, making it more robust and reliable in practical uncertain environments. Zonotopes, being geometric objects, allow efficient representation and propagation of uncertainty. This comparative study helps identify the advantages and limitations of each method and contributes to designing more reliable estimation frameworks for real-world nonlinear systems, such as those found in autonomous vehicles and robotic control.
