Objectives of Programme's Learning Outcomes

• Imagine, design, build and operate machines, equipment and processes to provide a solution to a complex problem of energy production, conversion and transmission by integrating the needs, constraints, context and technical, economic, societal, ethical and environmental issues.
• Implement a chosen solution in the form of a drawing, schematic, diagram or plan that conforms to standards, a model, a prototype, software and/or a digital model.
• Mobilize a structured set of scientific knowledge and skills and specialized techniques to respond, with expertise and adaptability, to the missions of the civil engineer in energy engineering, specialized in the production and use of energy in industry.
• Master and appropriately mobilize knowledge, models, methods and techniques related to mechanical, electrical, chemical and materials engineering to solve energy problems related to production and use in industry.
• Study a piece of equipment or a process of energy production and use, critically selecting theories, models and methodological approaches, and considering multidisciplinary aspects such as material, environmental and energy performance.

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 by pole placement or by the LQR approach; develop Luenberger and Kalman observers; estimate the parameters of state-space representations; consider the concepts of uncertainty and robustness; apply these approaches to various systems and in particular to energy systems.

UE Content: description and pedagogical relevance

State equations of continuous-time and discrete-time systems; similarity transformation; canonical forms; stability analysis; commandability; observability; parameter identification; state feedback control; LQR control;  state estimation; Luenberger observer; Kalman filtering;  

This teaching unit brings together the basic notions of analysis, control, and state estimation of multivariable systems (AA Control of Multivariable Systems) with more advanced concepts in control and state estimation applied to energy systems in the broad sense (AA Control of Energy Systems). These also allow you to revisit concepts introduced in the first part of the course.

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
• Practical exam - Face-to-face

Q2 UE Assessment Comments

The oral exam includes the exam of Control of Multivariable Systems and Control of Energy Systems. This exam represents 80% of the global score.
Evaluation in relation with the practical work (20% of the global score)

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

• Oral examination - Face-to-face

Q3 UE Assessment Comments

The oral exam includes the exam of Control of Multivariable Systems and Control of Energy Systems. This exam represents 80% of the global score.