Summary

The objective is to enable students to design advanced digital solutions for the control and the coordination of distributed dynamic systems, such as production or distribution energy systems, as well as intelligent transportation systems.

Content

Keywords

Multivariable systems, complex systems, state-space methods, optimal control, LQR, dynamic programming, state-space observer, state estimation, coordination, navigation functions

Learning Prerequisites

Important concepts to start the course

Linear Algebra

Dynamic Systems

Learning Outcomes

By the end of the course, the student must be able to:

• Choose analysis, control or estimation approaches
• Design state-space controllers or estimators
• Justify selected approaches
• Argue on their pros and cons
• Design Control algorithms
• Apply Matlab or other scientific programming tools

Transversal skills

• Use a work methodology appropriate to the task.
• Take responsibility for environmental impacts of her/ his actions and decisions.
• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Manage priorities.
• Use both general and domain specific IT resources and tools
• Write a scientific or technical report.

Teaching methods

Lectures and case studies carried out individually

Assessment methods

Written exam and case study reports

Resources

Ressources en bibliothèque

• Multivariable control and coordination systems: course notes / Gillet

Notes/Handbook

Dynamic Coordination, Denis Gillet, September 2019