Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After completing the course, the student
• Understands what networked multi-agent systems are and can identify their building blocks
• Understands the challenges of controlling multi-agent systems over communication networks
• Recognizes application areas of networked multi-agent systems
• Can apply knowledge from the course to design multi-agent systems that are connected over different types of networks
• Can analyze and evaluate existing designs of networked multi-agent systems
• Can design and analyze control systems that are connected over different types of communication channels
• Understands the challenges when applying machine learning methods to NCSs
Through the course, the student will have the opportunity to improve several skills not related to the knowledge of the field, such as:
• Analytical thinking and problem solving
• Learn to search for research reports and articles on his/her project topic
• Team working
• Presentation skills

Credits: 5

Schedule: 28.02.2024 - 05.06.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Dominik Baumann

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    • Overview and Introduction
    • Communication System Models
    • Control System Models
    • Models of Networked Multi-agent Systems
    • Analysis and Design of Networked Multi-agent Systems
    • Resource-aware Control
    • Machine Learning in NCSs

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Homework assignments, group project (assessment includes work in the group, final reports and presentations, and engagement in discussions when other groups present their results)

  • valid for whole curriculum period:

    • Lectures and exercises
    • Homework assignments
    • Group project
      • Group work
      • Report writing
      • Presentation: presentation + discussion and preparation
      • Literature review


Study Material
  • valid for whole curriculum period:

    The course is new and there is no textbook that exactly covers the course content. Slides will be provided. Slides will be based on several text books, the core material will come from:
    • J. Lunze, Networked Control of Multi-Agent Systems
    • K. You, N. Xiao, L. Xie, Analysis and Design of Networked Control Systems (Chapters 1-8)
    • A. Bemporad, M. Heemels, M. Johansson, Networked Control Systems

Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2023-2024 Spring IV - V