Programme overview
The programme below is according to the Education and Examination Regulations of the year 2022/2023.
The first year of the programme consists of compulsory courses, profile specific courses and electives. The second year consists of practical work: the internship and the Graduation Project.
Part | Study load (EC) |
Core programme | 30 |
Profile specific and elective subjects | 30 |
Internship / Graduation Project | 60 |
Core programme
Students must complete the core programme shown below, totalling 30 credits. The core programs of the 3 universities are interchangeable, in the sense that all credits for core courses obtained in the Systems and Control master program at one university will be accepted when a student transfers to the MSc program in Systems and Control at one of the other universities.
1. Students must complete the courses on the table below, totalling 25EC.
Code | Course | Study load (EC) | Quarter |
201900093 | Control System Design for Robotics | 5 | 1B |
201900007 | Perspectives on Engineering Design | 2,5 | 1B |
201100137 | Philosophy of Engineering: Ethics | 2,5 | 1B |
191131700 | System Identification with Parameter Estimation and Machine Learning | 5 | 2B |
200900012 | Integration project*) | 5 | Year |
one of two courses (to be chosen by the student): | |||
191211110 | Modelling and Simulation**) | 5 | 2B |
202200101 | Modelling, Dynamics, and Kinematics | 5 | 1A |
*)The Integration Project is a “year course”, which can be taken during any quarter. However it requires prior knowledge of the other compulsory courses, which is a reason to attend it during Q2B.The teacher, will take care that approximately 80% of the project can be carried out during Q2A, so that only some specific topics have to be completed in Q2B. This way, an additional elective can be taken during Q2B.
**) The course Modelling and Simulation requires the course 202200101Modelling, Dynamics, and Kinematics or 191210431 Engineering System Dynamics as prior knowledge. Engineering System Dynamics can be taken during Quarter 1B as a part of the Electrical Engineering bachelor module 201700145 Systems and Control.
2.In addition students must complete one course in the field of control to be chosen from the following courses:
Code | Course | Study load (EC) | Quarter |
191561620 | Optimal Control | 5 | 1B |
202000256 | Learning and adaptive control | 5 | 2A |
191150480 | Human movement control | 5 | 2A |
201900085 | Nonlinear Control | 5 | 2A |
191560671 | Robust Control | 5 | 2A |
201700173 | Control for UAVs | 5 | 2B |
191561560 | Systems and Control1,2) | 6 | 1A |
The Integration Project can only be carried out, if the three technical core courses have been attended. However it is possible to carry it out during quarter 2B, in parallel with Modelling and Simulation.
1) Course of the mastermath Network Utrecht
2) In case this course is chosen, the core programme will contain 31EC.
Specialisations and specialisation-linked subjects
The following specialisations are offered at the University of Twente:
1. Robotics and Mechatronics
2. Control Theory
3. Bio-mechatronics
4. Unmanned Aerial Vehicles
The core programme is complemented with a total of 30 credits of profile specific courses and elective subjects. In consultation with the programme mentor, courses from all three universities can be chosen. Depending on the chair, in which the student will carry out the graduation project, additional requirements can be posed to the courses of the programme (for this, see also the pages of the specializations). Available courses at the University of Twente are listed in the table below. Lists of available courses at the Technical Universities of Delft and Eindhoven are maintained in their Implementation Regulations and are made public at their website. The total course programme of 60 credits has to be approved by the Examination Board.
Courses, not on one of the course lists, can be chosen but should be explicitly approved by the Examination Board.
List of specialisation-linked and Elective courses
Note that some of the courses may have an overlapping content, which may be a reason that they cannot be chosen in the same course list**.
Course code | Course name | EC | RM | CT | BM | UAV | Quarter |
|---|---|---|---|---|---|---|---|
191157750 | Engineering Acoustics | 5 | x | 1A | |||
202200103 | Image Processing and Computer Vision | 5 | x | x | 1A | ||
201600070 | Machine Learning I | 5 | x | x | x | x | 1A |
191210930 | Measurement Systems for Mechatronics | 5 | x | x | 1A | ||
202200101 | Modelling, Dynamics & Kinematics | 5 | x | x | 1A | ||
191561560 | Systems and Control*) | 6 | x | 1A | |||
202200100 | Systems Engineering | 5 | x | 1A | |||
201400427 | Transducer Science | 5 | x | x | x | 1A | |
201700171 | Aerodynamics and Flight Dynamics | 5 | x | x | 1B | ||
201800177 | Deep Learning - From Theory to Practice | 5 | x | x | x | x | 1B |
201500009 | Electric Vehicle System Design | 5 | x | x | x | 1B | |
201900037 | Flexible Multibody Dynamics | 5 | x | x | x | 1B | |
201900120 | Learning and adaptive control | 5 | x | x | 2A | ||
201600071 | Machine Learning II | 5 | x | x | x | x | 1B |
191561620 | Optimal Control | 5 | x | x | 1B | ||
202200105 | Robot Perception, Cognition, and Navigation | 5 | x | x | 1B | ||
201300004 | Robotics for Medical Applications | 5 | x | 1B | |||
202200109 | Advanced Software Development for Robotics | 5 | x | x | 2A | ||
202200107 | Design Principles for Robotic and Mechatronic Mechanisms | 5 | x | 2A | |||
191150480 | Human movement control | 5 | x | 2A | |||
201900097 | Machine learning in engineering | 5 | x | x | x | x | 2A |
201900085 | Nonlinear Control | 5 | x | x | x | x | 2A |
202200106 | Optimal Estimation for Dynamic Systems | 5 | x | x | x | 2A | |
201200135 | Random Signals and Filtering | 5 | x | x | 2A | ||
201700168 | Regulating robotics and drones | 2,5 | x | x | 2A | ||
191560671 | Robust Control | 5 | x | x | x | 2A | |
202200108 | Software Development for Robotics | 5 | x | x | x | 2A | |
202200110 | Tele-presence in Robotics | 5 | x | x | x | 2A | |
202200112 | AI for Autonomous Robots: deep learning and reinforcement learning | 5 | x | x | x | 2B | |
201700170 | Airborne Laser Scanning | 5 | x | x | 2B | ||
201200133 | Biomechatronics | 5 | x | 2B | |||
201700173 | Control for UAVs | 5 | x | x | 2B | ||
201000168 | Embedded Systems Laboratory | 5 | x | x | 2B | ||
201700071 | Identification of Human Physiological Systems | 5 | x | 2B | |||
202000040 | Introduction to Robotics Design | 5 | x | x | x | 2B | |
191571090 | Time Series Analysis | 5 | x | 2B |
*) Course of the Mastermath Network Utrecht
** The following combinations are not allowed due to considerable overlap in learning objectives:
1. The combination of:
· 201600070 Machine Learning I
· 202200112 AI for Autonomous Robots: deep learning and reinforcement learning
· 201900097 Machine learning in engineering
Systems and Control students are allowed take either of these courses, but not combine them.
2. Any of the combinations:
· 202200104 Control System Design for Robotics
· 201900089 Control for (B)ME
· 202000255 Advanced Control Engineering
· 191561560 Systems and Control
Control System Design for Robotics is compulsory, which means that Systems and Control students cannot take the other courses unless specifically requested and approved by the Examination Board.
3. The combination of:
· 191571090 Time Series Analysis
· 202200111 System Identification with Parameter Estimation and Machine Learning
System Identification with Parameter Estimation and Machine Learning is compulsory, which means that Systems and Control students cannot take Time Series Analysis unless specifically requested and approved by the Examination Board.
CT:Control Theory
RM: Robotics & Mechatronics
BM: Biomechatronics
UAV: Unmanned Arial Vehicles
