Master in Systems, Control and Robotics

General

Program Description

The master’s program in Systems, Control and Robotics equips students with the skills necessary to analyze, design and control complex technical systems such as robots, autonomous vehicles or any other system that has a significant autonomous capability. These systems are important today and will become even more important as new technology makes its way into our workplaces, homes and shared public spaces.

The master’s program starts with a few mandatory courses that provide a solid foundation in the area of systems, control, and robotics, as well as scientific methodology in general. The program continues with courses on one of two tracks chosen by the student. In addition to courses associated with the tracks, there are a large number of elective courses within the broad areas of AI, machine learning and control. In order to be prepared for a diversity of future careers, students will also take a project course and a non-technical course of choice, and participate in a seminar series in which an opportunity is provided to reflect on the societal context of systems, control, and robotics, such as sustainability and ethics.

Robotics and autonomous systems

This track focuses on autonomous mobile systems that act in a dynamic world, including robots, drones, and autonomous vehicles. Such systems need to act rationally based on information from complex sensors such as cameras or laser scanners in order to achieve both short- and long-term objectives in a changing world.

Learning, Decision and Control Systems

This track focuses on the analysis and synthesis of decision and control systems, both model-based, including classical control systems and AI, as well as data-driven systems, with machine learning components.

This is a two-year program (120 ECTS credits) in English. Graduates are awarded a Master of Science Degree. The program is given mainly at the KTH Campus in Stockholm by the School of Electrical Engineering and Computer Science (at KTH).

Master degree project

All students will carry out a degree project, typically during the second half of the second year, upon completion of the necessary program credits. The project may involve work in a relevant industry or in a department at KTH and may be combined with course work. For students who wish to pursue a career in research, the thesis project offers an excellent opportunity to develop contacts and the skills necessary to work within a research group; and for those students wishing to enter the industry, it serves as an important introduction and practical foundation for a career with a prospective employer.

Career

A two-year master’s degree in Systems, Control and Robotics rests on a core set of courses in systems, control and robotics and related subjects, and provides the opportunity to utilise a unique cross-section of courses from different disciplines – integrating, for example, Artificial Intelligence, Machine Learning and Control Theory – in order to create a comprehensive education. As systems, control and robotics engineers require extensive training in the design and analysis of complex technical systems, this master’s program provides a strong foundation in both theory and practice. Graduates from the program often end up in the robotic and autonomous vehicle industry, such as ABB and Scania, or as Ph.D. students at KTH and other universities.

Students

Find out what students from the program think about their time at KTH.

Anna Madlener, Germany: "One of my favorite memories so far at KTH was an individual project that another student and I were allowed to work on over an entire semester, where we worked with a robot and sonar system to map glacial fronts."

Sustainable development

Graduates from KTH have the knowledge and tools for moving society in a more sustainable direction, as sustainable development is an integral part of all programs. The three key sustainable development goals addressed by the master's program in Systems, Control and Robotics are:

  • Good Health and Well-Being
  • Decent Work
  • Industry, Innovation, and Infrastructure

It is the strong development in the area of Systems, Control, and Robotics that is currently making cars more autonomous, robotic production lines more efficient and safe for human workers, and elderly people more independent by providing adaptive robotic assistants. Thus, goal #3 (Good Health and Well-being) is addressed by safer factories and better care of the elderly. Goal #8 (Decent Work and Economic Growth) is addressed by safer and more efficient factories, and goal #9 (Industry, Innovation, and Infrastructure ) by a combination of autonomous transport and automated production lines in factories.

Courses

The two-year master's program in Systems, Control and Robotics consists of three terms of courses and one final term dedicated to the master's degree project. Each term consists of approximately 30 ECTS credits. Depending on which track you choose, you will study different courses. The courses presented on this page apply to studies starting in autumn 2020.

Year 1

Mandatory courses for all tracks

  • Introduction to Robotics (DD2410) 7.5 credits
  • The Sustainable Systems and Control Engineer (EL2220) 3.0 credits
  • Control Theory and Practice, Advanced Course (EL2520) 7.5 credits
  • Modeling of Dynamical Systems (EL2820) 7.5 credits

Recommended courses for all tracks

  • Deep Learning, Advanced Course (DD2412) 6.0 credits
  • Project Course in Robotics and Autonomous Systems (DD2419) 9.0 credits
  • Probabilistic Graphical Models (DD2420) 7.5 credits
  • Deep Learning in Data Science (DD2424) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Project Course in Data Science (DD2430) 7.5 credits
  • Artificial Intelligence and Multi-Agent Systems (DD2438) 15.0 credits
  • Human-Computer Interaction, Introductory Course (DH1620) 6.0 credits
  • Multimodal Interaction and Interfaces (DT2140) 7.5 credits
  • Project in Cognitive Systems (DT2150) 7.5 credits
  • Electricity Market Analysis (EG2210) 7.5 credits
  • Business Development and Quality Management (EH2030) 7.5 credits
  • Management of Projects (EH2720) 7.5 credits
  • Computer Applications in Power Systems (EH2745) 4.5 credits
  • Build your own Radar System, Project Course (EK2370) 7.5 credits
  • Automatic Control, General Course (EL1010) 6.0 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Model Predictive Control (EL2700) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Building Networked Systems Security (EP2520) 7.5 credits
  • Signal Theory (EQ1220) 7.5 credits
  • Digital Communications (EQ2310) 9.0 credits
  • Speech and Audio Processing (EQ2321) 7.5 credits
  • Cyber-Physical Networking (EQ2871) 7.5 credits
  • Sensor-Based Systems (II2302) 7.5 credits
  • Embedded Systems (IL2206) 7.5 credits
  • Embedded Software (IL2212) 7.5 credits
  • English for Employment (LS1419) 7.5 credits
  • Rhetoric - the Art of Persuasion (LS1464) 7.5 credits
  • Technical Communication in English (LS2429) 7.5 credits
  • English for Writing and Presenting a Degree Project in Science and Engineering (LS2439) 7.5 credits
  • Industrial Management, Basic Course (ME1003) 6.0 credits
  • Leadership in Cross-Cultural and Industrial Contexts (ME2089) 6.0 credits
  • Dynamics and Motion Control (MF2007) 9.0 credits
  • Mechatronics basic Course (MF2030) 6.0 credits
  • Robust Mechatronics (MF2043) 6.0 credits
  • Applied Vehicle Dynamics Control (SD2231) 7.5 credits
  • Complex Analysis (SF1691) 7.5 credits
  • Optimization (SF1811) 6.0 credits
  • Optimization (SF1861) 6.0 credits
  • Parallel Computations for Large- Scale Problems (SF2568) 7.5 credits
  • Applied Linear Optimization (SF2812) 7.5 credits
  • Mathematical Systems Theory (SF2832) 7.5 credits
  • Geometric Control Theory (SF2842) 7.5 credits
  • Probability Theory (SF2940) 7.5 credits
  • Time Series Analysis (SF2943) 7.5 credits

Learning, Decision and Control Systems track

Mandatory courses

  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Model Predictive Control (EL2700) 7.5 credits

Conditionally elective courses

  • Artificial Intelligence (DD2380) 6.0 credits
  • Machine Learning (DD2421) 7.5 credits
  • Deep Learning in Data Science (DD2424) 7.5 credits
  • Machine Learning, Advanced Course (DD2434) 7.5 credits
  • Automatic Control, Project Course (EL2421) 15.0 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Digital Signal Processing (EQ2300) 7.5 credits
  • Adaptive Signal Processing (EQ2401) 7.5 credits
  • Complex Analysis (SF1691) 7.5 credits
  • Applied Nonlinear Optimization (SF2822) 7.5 credits
  • Mathematical Systems Theory (SF2832) 7.5 credits
  • Geometric Control Theory (SF2842) 7.5 credits

Robotics and Autonomous Systems track

Mandatory courses

  • Image Analysis and Computer Vision (DD2423) 7.5 credits
  • Applied Estimation (EL2320) 7.5 credits

Conditionally elective courses

  • Artificial Intelligence (DD2380) 6.0 credits
  • Research project in Robotics, Perception, and Learning (DD2411) 15.0 credits
  • Project Course in Robotics and Autonomous Systems (DD2419) 9.0 credits
  • Machine Learning (DD2421) 7.5 credits
  • Deep Learning in Data Science (DD2424) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Computational Photography (DD2429) 6.0 credits
  • Machine Learning, Advanced Course (DD2434) 7.5 credits
  • Artificial Neural Networks and Deep Architectures (DD2437) 7.5 credits
  • Speech and Speaker Recognition (DT2119) 7.5 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Model Predictive Control (EL2700) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Digital Signal Processing (EQ2300) 7.5 credits
  • Speech and Audio Processing (EQ2321) 7.5 credits
  • Dynamics and Motion Control (MF2007) 9.0 credits

Year 2

Mandatory courses for all tracks

  • Theory and Methodology of Science with Applications (Natural and Technological Science) (AK2036) 7.5 credits
  • The Sustainable Systems and Control Engineer (EL2220) 3.0 credits

Recommended courses for all tracks

  • Software Engineering (DD1385) 6.0 credits
  • Program System Construction Using C++ (DD1388) 7.5 credits
  • Algorithms and Complexity (DD2352) 7.5 credits
  • Neuroscience (DD2401) 7.5 credits
  • Probabilistic Graphical Models (DD2420) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Mathematical Modelling of Biological Systems (DD2435) 9.0 credits
  • Artificial Intelligence and Multi-Agent Systems (DD2438) 15.0 credits
  • Statistical Methods in Applied Computer Science (DD2447) 6.0 credits
  • Software Reliability (DD2459) 7.5 credits
  • Bigger Advanced, Individual Course in Computer Science (DD2464) 9.0 credits
  • Search Engines and Information Retrieval Systems (DD2476) 9.0 credits
  • Human-Computer Interaction, Introductory Course (DH1620) 6.0 credits
  • Multimodal Interaction and Interfaces (DT2140) 7.5 credits
  • Electricity Market Analysis (EG2210) 7.5 credits
  • Business Development and Quality Management (EH2030) 7.5 credits
  • Management of Projects (EH2720) 7.5 credits
  • Computer Applications in Power Systems (EH2745) 4.5 credits
  • Build your own Radar System, Project Course (EK2370) 7.5 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Model Predictive Control (EL2700) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Building Networked Systems Security (EP2520) 7.5 credits
  • Digital Communications (EQ2310) 9.0 credits
  • Speech and Audio Processing (EQ2321) 7.5 credits
  • Adaptive Signal Processing (EQ2401) 7.5 credits
  • Cyber-Physical Networking (EQ2871) 7.5 credits
  • Sensor-Based Systems (II2302) 7.5 credits
  • Embedded Systems (IL2206) 7.5 credits
  • Embedded Software (IL2212) 7.5 credits
  • English for Employment (LS1419) 7.5 credits
  • Rhetoric - the Art of Persuasion (LS1464) 7.5 credits
  • German B2 for Engineers (LS2426) 7.5 credits
  • Technical Communication in English (LS2429) 7.5 credits
  • French B2 for Engineers (LS2436) 7.5 credits
  • English for Writing and Presenting a Degree Project in Science and Engineering (LS2439) 7.5 credits
  • Spanish B2 for Engineers (LS2449) 7.5 credits
  • Industrial Management, Basic Course (ME1003) 6.0 credits
  • Leadership in Cross-Cultural and Industrial Contexts (ME2089) 6.0 credits
  • Dynamics and Motion Control (MF2007) 9.0 credits
  • Mechatronics basic Course (MF2030) 6.0 credits
  • Robust Mechatronics (MF2043) 6.0 credits
  • Applied Vehicle Dynamics Control (SD2231) 7.5 credits
  • Complex Analysis (SF1691) 7.5 credits
  • Optimization (SF1811) 6.0 credits
  • Optimization (SF1861) 6.0 credits
  • Parallel Computations for Large- Scale Problems (SF2568) 7.5 credits
  • Applied Linear Optimization (SF2812) 7.5 credits
  • Mathematical Systems Theory (SF2832) 7.5 credits
  • Geometric Control Theory (SF2842) 7.5 credits
  • Optimal Control Theory (SF2852) 7.5 credits
  • Probability Theory (SF2940) 7.5 credits
  • Time Series Analysis (SF2943) 7.5 credits

Learning, Decision and Control Systems track

Conditionally elective courses

  • Artificial Intelligence (DD2380) 6.0 credits
  • Machine Learning (DD2421) 7.5 credits
  • Deep Learning in Data Science (DD2424) 7.5 credits
  • Machine Learning, Advanced Course (DD2434) 7.5 credits
  • Automatic Control, Project Course (EL2421) 15.0 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Digital Signal Processing (EQ2300) 7.5 credits
  • Adaptive Signal Processing (EQ2401) 7.5 credits
  • Optimal Filtering (EQ2801) 7.5 credits
  • Complex Analysis (SF1691) 7.5 credits
  • Applied Nonlinear Optimization (SF2822) 7.5 credits
  • Mathematical Systems Theory (SF2832) 7.5 credits
  • Geometric Control Theory (SF2842) 7.5 credits
  • Optimal Control Theory (SF2852) 7.5 credits

Robotics and Autonomous Systems track

Conditionally elective courses

  • Artificial Intelligence (DD2380) 6.0 credits
  • Research project in Robotics, Perception, and Learning (DD2411) 15.0 credits
  • Project Course in Robotics and Autonomous Systems (DD2419) 9.0 credits
  • Machine Learning (DD2421) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Computational Photography (DD2429) 6.0 credits
  • Machine Learning, Advanced Course (DD2434) 7.5 credits
  • Artificial Neural Networks and Deep Architectures (DD2437) 7.5 credits
  • Automatic Control, Project Course, Smaller Course (EL2425) 7.5 credits
  • Hybrid and Embedded Control Systems (EL2450) 7.5 credits
  • Nonlinear Control (EL2620) 7.5 credits
  • Model Predictive Control (EL2700) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Digital Signal Processing (EQ2300) 7.5 credits
  • Speech and Audio Processing (EQ2321) 7.5 credits
  • Dynamics and Motion Control (MF2007) 9.0 credits

Admission requirements

To be eligible for the program, you must have been awarded a bachelor's degree, be proficient in English and meet the program-specific requirements.

Bachelor's degree

A bachelor's degree, equivalent to a Swedish bachelor's degree, or equivalent academic qualifications from an internationally recognized university, is required. Students who are following longer technical programs, and have completed courses equivalent to a bachelor's degree, will be considered on a case-by-case basis.

English proficiency

English language proficiency equivalent to (the Swedish upper secondary school) English course B/6 is required. The requirement can be satisfied through a result equal to, or higher than, those stated in the following internationally recognized English tests:

  • TOEFL Paper-based: Score of 4.5 (scale 1-6) in written test, a total score of 575.
    TOEFL ITP is not accepted.
  • TOEFL iBT internet-based: Score of 20 (scale 0-30) in written test, a total score of 90
  • IELTS Academic: A minimum overall mark of 6.5, with no section lower than 5.5
  • Cambridge ESOL: Cambridge English: Advanced (CAE) Certificate in Advanced English or Cambridge English: Proficiency (CPE) (Certificate of Proficiency in English)
  • Michigan English Language Assessment Battery (MELAB): Minimum score of 90
  • The University of Michigan, ECPE (Examination for the Certificate of Proficiency in English)
  • Pearson PTE Academic: Score of 62 (writing 61)

Specific requirements for the master's program in Systems, Control and Robotics

A bachelor's degree, corresponding to 180 ECTS credits, including basic mathematics courses in linear algebra, calculus in one and several variables, probability theory, and computer science. In addition, a course in signals and systems including material about time-continuous and time-discrete systems, sampling, linear filters, and systems, transform methods (Laplace and Z) and a course in control engineering is required. The above can also be described as the student to have completed courses corresponding to the following KTH courses:

  • SF1624 Algebra and Geometry
  • SF1625 Calculus in One Variable
  • SF1626 Calculus in Several Variables
  • SF1920 Probability Theory and Statistics
  • SF1683 Differential Equations and Transforms
  • EL1000 Automatic Control, General Course
  • DD1315 Programming Techniques

Application documents

  1. Certificates and diplomas from previous university studies
  2. Transcript of completed courses and grades included in your degree
  3. Proof of English proficiency
  4. A copy of your passport including personal data and photograph, or other identification documents

Specific documents for the master's program in Systems, Control and Robotics

  • Letter of motivation explaining why you are interested in this program.
  • Two letters of recommendation (in English).
  • Work experience, if any, with relevance to the subject/field of the program.
  • Curriculum Vitae
  • Summary Sheet for Systems, Control and Robotics, KTH (Starting 2020) *

*In order for your application to be considered complete, you need to fill out the online summary sheet. If you do not include a summary sheet, this may negatively affect your evaluation score. Please be sure to fill out all of the required information before you submit the form.

Last updated Apr 2020

About the School

KTH Royal Institute of Technology has served as one of Europe’s key centres of innovation and intellectual talent for almost two hundred years. Recognized as Sweden’s most prestigious technical univer ... Read More

KTH Royal Institute of Technology has served as one of Europe’s key centres of innovation and intellectual talent for almost two hundred years. Recognized as Sweden’s most prestigious technical university, KTH is also the country’s oldest and largest. With over 12,000 students and an international reputation for excellence, the university continues to nurture the world’s brightest minds, helping to shape the future. Read less