Course content

Introduction to automation-relevant topics and terminology. Introduction to control theory using simple examples. Introduction of phenomena and concepts in dynamic (time variable) systems. Modelling and simulation of simple physical systems. Introduction to numerical tools (MATLAB and Simulink), microcontrollers (e.g. Arduino), robotics (kinematics), and artificial intelligence (machine learning and artificial neural networks). Other topics are possible.

Learning outcome

Knowledge: The candidate can explain

• the general structure of a control system;
• the behavior of controllers with P, I and D action;
• about the origin and effect of nonlinearities on a control system;
• control principles such as feedback and feed-forward control;
• how a simple artificial neural network works.

Skills: The candidate can

• model physical systems with first- and second-order differential equations;
• simulate dynamic systems using numerical tools;
• analyze a dynamical system consisting of a first- or second-order differential equation;
• solve simple kinematics problems;
• design simple regulators and tune them;
• carry out smaller control engineering projects, both individually and in a team.

General competence: Communicate about professional issues both with specialists and the general public. Basic system understanding.

Digitalization: The course contributes by enabling the candidate to use modern computer tools as an aid in all mentioned skill moments.

Learning methods and activities

Lectures, exercises, laboratory assignments and a practical project.

Compulsory assignments

• Exercises

Specific conditions

Recommended previous knowledge

None.

Required previous knowledge

For applications for credentials, approval and incorporating of courses from other institutions' corresponding education programs, each application will be processed individually and the applicant should expect credit reductions for overlapping courses.

Course materials

Textbook is announced at the start of the semester.