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 an essential measurement and control vector in modern society by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Identify complex problems to be solved and develop the specifications with the client by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Based on modelling and experimentation, design one or more systems/solutions/software addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Implement a chosen system/solution in the form of a schema, a flowchart, an algorithm, a prototype, software and/or digital model.
• Evaluate the approach and results for their adaptation (optimisation and quality).
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out electrical engineering missions, with a focus on signals and systems, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques related to the basics of electricity, electronics, automatic control, signal processing and analysis, telecommunications, hardware and software instrumentation, mathematical modelling and analysis of dynamic systems, control methods, more specific applications related to signal processing, and control of biomedical and biological systems.
• Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.

Learning Outcomes of UE

This course focuses on dynamic systems with discrete states and transitions for the modeling of technological systems such as automated production systems and process control systems and transport systems.

The practical part of the course is related to a concrete application of modeling, control and opimazation of a complex system such as a production line with multiple units, making use of the ExtendSim software.

UE Content: description and pedagogical relevance

Time-driven vs. event-driven systems, examples of Discrete Event Systems (DES): automated manufacturing, traffic systems, the queueing system model.
Models of DES, Basic concepts in discrete event simulation, Monte-Carlo simulation, Input modeling and output analysis, model construction and applications, study of alternative system configurations.
Basic concepts of Agent-Based Models (ABM).
Grafcet Model and process design.

Prior Experience

Not applicable