Objectives of Programme's Learning Outcomes

• Imagine, implement and operate systems/solutions/software to deal with a complex problem in the field of electricity by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Implement a chosen system/solution/software/circuit in the form of a diagram, flow chart, algorithm, plan, model, prototype, program, software and/or digital model.
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out electrical engineering missions, using their expertise and adaptability.
• Master and mobilise knowledge, models, methods and techniques relating to the basics of electricity, electronics, automation, signal analysis and processing, telecommunications; modern electrical network engineering (production, transport, distribution); electric vehicles; advanced electronic systems; wired and wireless telecommunications; intelligent sensors; human-machine interfaces; mathematical modelling and analysis of dynamic systems; process control; image processing and processing; and the use of the Internet; advanced electronic systems; wired and wireless telecommunications; intelligent sensors; human-machine interfaces; mathematical modelling and analysis of dynamic systems; process control; image and sound processing and, more s
• Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
• Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
• Master technical English in the field of electrical engineering.

Learning Outcomes of UE

Describe the dynamic behavior of multiple-input multiple-output (MIMO) processes using state equations or matrices of transfer functions; study the properties of these systems: stability, controllability and observability; use the notion of similarity transformation to put the state space representation into a canonical form; design state feedback controllers and Luenberger observers; estimate the parameters of state-space representations; consider the concepts of uncertainty and robustness; apply these approaches either to continuous or discrete-time systems.

UE Content: description and pedagogical relevance

State equations of continuous-time and discrete-time systems; stability analysis; commandability; observability; state feedback control; state estimation; Luenberger observer; identification;

Prior Experience

differential equations, transfer functions, PID controller