Teaching & Student Projects

Learning outcomes:

After completing the course the student:

• understands the principles of discrete-time modelling and computer control
• understands the common ideas and differences between analog and digital control
• can design, simulate and implement discrete-time controllers (for example discretized PID or state feedback controllers)
• understands the Principle of Optimality
• understands the ideas behind optimal controllers, specifically LQ control
• can design and implement LQ controllers
• understands the basics of stochastic control theory

Learning outcomes:

After completing the course the student:

• understands what a Networked Control System (NCS) is, identifying the building blocks of control and communications comprising NCSs
• recognizes applications of NCSs, identifying their building blocks
• designs and analyzes simple control systems over different communication channels, building further understanding of the interplay between communications and control
• understands the estimation issues of systems with uncertainties having to communicate over noisy communication channels
• learns when control and estimation problems can be decoupled in such systems
• understands the limitations of the current state-of-the-art and trends in NCSs

Learning outcomes:

After completing the course the student:

• understands the principles and analysis methods of dynamical systems
• can design controllers by different methods and verify the operation of the closed loop system by analytical means and through simulation
• understands the hardware and software architectures of automation systems
• can use automation programming languages of PLCs
• designs methods and patterns learned with hands on experience

The available M.Sc. thesis topics from Control, Robotics and Autonomous Systems (CRAS) major can be found here. If you are interested in a different topic, you are more than welcome to propose your own project.