Course contents
The course introduces and gives examples of mechatronic products and the various components, design alternatives, methods and tools used in mechatronics design. Real mechatronics design problems are identified and solved.
Course memo Autumn 2026-10602
Version 1 – 09/17/2026, 5:25:50 PM
Autumn 2026-10602 (Start date 24 Aug 2026, English)
English
ITM/Mechatronics
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2019
The course introduces and gives examples of mechatronic products and the various components, design alternatives, methods and tools used in mechatronics design. Real mechatronics design problems are identified and solved.
After passing the course, the students should be able to:
The course combines lectures, tutorials, independent study and a group project. The lectures introduce the principal course content and are supported by specified parts of the course book, lecture notes, examples, exercises and formative quizzes.
The tutorials reinforce and clarify the lecture content through the modeling and simulation of interactive examples based on mechatronic components and systems.
The group project concerns the analysis and design of an established mechatronic product used in industry. Students work in groups of three to model, simulate, control, and analyze the system. Scheduled project sessions provide time for teamwork and guidance from teaching assistants. The project sessions are intended for project work and consultation and are not additional tutorials.
Attendance at lectures, tutorials and project sessions is not compulsory, but is recommended. Students are strongly recommended to use the scheduled project sessions for teamwork and guidance.
The course is based on:
The lectures introduce the fundamentals of mechatronics, mechatronic systems design, modeling and control, multibody dynamics, transducers, information processing, and implementation aspects. The tutorials reinforce the lecture content through modeling, simulation, and control examples. The group project applies these topics to the analysis and design of an established mechatronic product used in industry.
Dates, times and locations are provided in the official MF2030 schedule. Students should use the schedule as the authoritative source for current activity and examination details.
Students are expected to have:
The following knowledge is beneficial but is not a prerequisite and will be covered during the course:
Janschek, Klaus. Mechatronic Systems Design: Methods, Models, Concepts. 1st edition. Springer, 2012. ISBN 978-3-642-17530-5 (print) and 978-3-642-17531-2 (electronic).
Selected parts of the book are used and are specified in connection with the respective lecture or tutorial.
The electronic book is available through the KTH Library and directly from the publisher.
Additional study material includes lecture notes, tutorial material, the group assignment, examples, exercises, formative quizzes, simulation files and supplementary guidance. Registered students receive this material in the course room.
MATLAB and Simulink are used for modeling, simulation and analysis. Students are recommended to arrange access before the course begins. Installation on a personal computer is recommended but not mandatory, because the web-based versions can also be used.
KTH students can sign in through the KTH MathWorks portal to download and activate MATLAB and Simulink using the KTH licence.
The web-based versions are available through MATLAB Online.
Students at KTH with a permanent disability can get support during studies from Funka:
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
The section below is not retrieved from the course syllabus:
The hand-in assignment is a modeling, simulation, control and analysis project completed in groups of three students. Each group submits one written report electronically. The assignment requires work outside the scheduled project sessions.
The submitted work is assessed with regard to technical correctness, internal consistency, justified assumptions and engineering choices, functioning implementations, appropriate verification and analysis, interpretation of results, and clear presentation. Alternative modeling or control approaches are accepted when they are technically correct, internally consistent and properly motivated.
All members of the group are responsible for the complete submitted work and must be able to present, explain and answer questions about the complete assignment and solution. Collaboration within the assigned project group is permitted and expected.
The submission deadline for Autumn 2026 is 14 October 2026 at 23:59. Detailed task requirements and submission instructions are provided to registered students with the assignment.
Assignments should be submitted by the stated deadline. If an assignment is submitted late without an accepted justification, the group may be required to complete additional or complementary tasks in order to pass INL1. Students who expect difficulty meeting the deadline should contact the examiner as early as possible.
If a submission is assessed as failing but reasonably close to the requirements for a pass, the group may be given an opportunity to complement the work.
Use of generative AI in INL1
Generative AI tools may be used only in accordance with the course guidelines and the additional instructions provided with the assignment. Generative AI may support learning, exploration, self-evaluation or language revision, but it must not perform the engineering reasoning, modeling, analysis, design decisions, interpretation of results or formulation of conclusions that the assignment assesses. It may not be used to generate complete assignment solutions or substantial sections of the report.
The findings, structure, technical content, reasoning and conclusions must be the group's own. Students are responsible for critically evaluating and, where appropriate, verifying AI-generated information. The group must be able to defend and explain all submitted material without support from generative AI.
The report must include an Acknowledgements section stating whether generative AI tools were used. If they were used, the section must identify the tool or tools and briefly describe the nature and extent of their use. Undisclosed or unauthorized use may be treated as an attempt to mislead and may lead to disciplinary action.
TEN1 is an individual written examination. The examination assesses the student's understanding of the course concepts and ability to describe, model, analyze and reason about mechatronic systems and components. The main written examination is scheduled for 21 October 2026. Students should confirm the time and location in the official course schedule.
Permitted aids:
No other aids are permitted. The use of generative AI tools during the written examination is strictly prohibited.
A student who receives Fx on TEN1 may complete a complementary task set by the examiner at a time agreed between the examiner and the student. Successful completion results in grade E. A higher grade cannot be obtained through this complementary task.
The student should contact the examiner as soon as possible after the examination result has been released and, where possible, before the result is attested in Ladok.
A student who has passed TEN1 may apply to take the scheduled December re-examination in order to attempt to raise the grade. Grade raising is not offered at the main examination.
The application must be submitted during the registration period for the relevant examination. Students should follow the current KTH instructions and application procedure.
Current information and the application procedure are available on KTH's grade raising page.
The rules for generative AI depend on the course activity and examination component. Where generative AI is permitted, its use must be responsible, ethical and academically honest. It must support rather than replace the student's own learning and engineering reasoning. Information obtained from generative AI should be critically evaluated and, where appropriate, verified using course resources or other reliable sources.
Generative AI may be used in non-graded activities to gain an initial overview, explore concepts, formulate questions, create practice material or refine the language of text written by the student. Students remain fully responsible for all material they submit and should ask the course responsible for guidance if they are uncertain whether a particular use is permitted.
24 Aug 2026
English