UE E9AM2AS-A - Tools and Software for Automatic Control (OLA)

Level of knowledge :

N1: beginner
N2: intermediate
N3: advanced
N4: expert

The knowledge expected at the end of the course

The aim of this course is to enable students to master the mathematical and computational tools required in Automatic Control. These tools will subsequently be used in design offices, projects and internships: - Optimization - Random Processes - Bond Graph Modeling, Kalman Filtering, Non-Integer Derivative Systems, Artificial Intelligence and the Model Approach in Automatic Control.



Acquire basic knowledge of non-integer derivative systems, theories and applications (C1, N2), (C2, N2), (C3, N2), (C6, N2)


Acquire knowledge of linear and non-linear optimization methods (C1, N2), (C2, N2), (C3, N2), (C6, N2)


Acquire skills in Bond Graph modeling (C1, N2), (C2, N2), (C3, N2), (C6, N2)


Acquire skills in the properties and tools of random process processing and information theory (C1, N2), (C2, N2), (C3, N2), (C6, N2)


Learning outcomes in terms of abilities, skills and attitudes expected at the end of EU courses

Learn to analyze non-integer systems and apply the associated mathematical tools (C3, N2), (C6, N2)
Apply linear optimization methods to an application example (C3, N2), (C4, N2), (C5, N2), (C6, N2)
Apply non-linear optimization methods to an application example (C3, N2), (C4, N2), (C5, N2), (C6, N2)
Create a simulation model from a Bond Graph (C3, N2), (C4, N2), (C5, N2), (C6, N2)
Learn about the system engineering approach by implementing the simulation model to be developed in Bond Graph in Matlab/Simulink (C3, N2), (C4, N2), (C5, N2), (C6, N2)
Learn how to handle random process processing and information theory tools in a design office (C3, N2), (C4, N2), (C5, N2), (C6, N2)