Cette matière a pour objectif de permettre aux étudiants de maîtriser les outils de modélisation et les techniques de contrôle des robots manipulateurs. Elle vise à donner aux étudiants la possibilité d’entreprendre en toute autonomie la résolution d’un certain nombre de problèmes élémentaires de robotique comme la mise en configuration, la génération de trajectoires, la commande dynamique.

The aim of this course is to study different diagnostic methods that involve the detection and isolation of faults, including both model-based and model-free approaches. We will explore how to enhance the performance of dynamic systems by ensuring better reliability. The objective is to understand how to identify and address potential issues to optimize system operation. By developing these diagnostic skills, we can strengthen system reliability and ensure optimal performance.

This course offers an immersive and comprehensive exploration into intelligent control systems, uniquely blending the intricate concepts of fuzzy logic with the dynamic capabilities of artificial neural networks (ANNs). Tailored for Master 2 students in Automatic and Systems Engineering, it promises a profound understanding of both the theoretical underpinnings and practical implementations of these advanced technologies in control systems and engineering.

Key objectives include mastering the fundamentals of fuzzy logic, with a focus on its role in modeling uncertainty in control systems, and gaining a robust grasp of ANNs, encompassing their architecture, learning algorithms, and applications in intelligent control. A pivotal aspect of the course is the exploration of integrating fuzzy logic with ANNs, delving into hybrid systems that synergize the strengths of both methodologies