Objectives of Programme's Learning Outcomes

• Imagine, implement and operate systems/solutions/software to address a complex problem in the field of electrical engineering as a source of information by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Implement a chosen system/solution/software in the form of a drawing, a schema, a flowchart, an algorithm, a plan, a model, a prototype, 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 appropriately mobilise knowledge, models, methods and techniques specific to electrical engineering.
• 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 behaviour of multiple-input multiple-output (MIMO) processes using state equations; linearize nonlinear state space equations; compute equilibrium points and analyze the local stability; compute the analytic solution of linear state space models; transform state space representation into canonical forms; compute matrices of transfert functions;  analyze controllability and observability of linear systems; design state feedback controllers and Luenberger observers; estimate the parameters of state-space representations; analyze input-output coupling and pairing using the relative gain array; compute decentralized control structures using PID controllers; apply these approaches either to continuous or discrete time systems.

Content of UE

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

Prior Experience

differential equations, transfer functions, PID controller

Type of Assessment for UE in Q2

• Oral Examination

Q2 UE Assessment Comments

Oral exam covering both theory (weighting 2/3) and exercises (weighting 1/3) with written preparation (the exam represents 90% of the overall score).
Evaluation of practical work sessions (10% of the overall score).

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Oral exam covering both theory (weighting 2/3) and exercises (weighting 1/3) with written preparation (the examen represents 90% of the overall score).