Objectives of Programme's Learning Outcomes

• Imagine, design, carry out and operate solutions (machines, equipment, processes, systems and units) to provide a solution to a complex problem by integrating needs, constraints, context and technical, economic, societal, ethical and environmental issues.
• Implement a chosen solution in the form of a drawing, a schema, a diagram or a plan that complies with standards, 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 mechanical engineering missions, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques specific to mechanical engineering.
• Study a machine, equipment, system, method or device by critically selecting theories and methodological approaches, and taking into account multidisciplinary aspects.

Learning Outcomes of UE

Describe the dynamic behavior of multiple-input multiple-output (MIMO) processes using deterministic or stochastic state equations; compute equilibrium points and analyze stability; transform state space representation into canonical forms; compute matrices of transfert functions;  analyze controllability and observability of linear systems; design state feedback controllers; design Luenberger observers, Kalman filters and receding horizon observers; apply these approaches either to continuous or discrete-time systems.

UE Content: description and pedagogical relevance

deterministic and stochastic state equations of continuous-time and discrete-time systems; stability analysis; commandability; observability; state feedback control; state estimation; Luenberger observer; Kalman filter and extensions; receding-horizon observers; identification;

Prior Experience

differential equations, transfer functions, PID controller.

Type(s) and mode(s) of Q2 UE assessment

• Production (written work, report, essay, collection, product, etc.) - To be submitted online
• Oral examination - Face-to-face

Q2 UE Assessment Comments

Joint exam for Control of Multivariable Systems and Optimal Estimation.
Oral exam covering both theory (weighting 2/3) and exercises (weighting 1/3) with written preparation (exam represents 80% of the overall score).
Evaluation in relation to practical work sessions (20% of the overall score).

Type(s) and mode(s) of Q3 UE assessment

• Oral examination - Face-to-face

Q3 UE Assessment Comments

Joint exam for Control of Multivariable Systems and Optimal Estimation.
Oral exam covering both theory (weighting 2/3) and exercises (weighting 1/3) with written preparation (exam represents 80% of the overall score).