Programplaner og emneplaner - Student
ACIT4810 Advanced Methods in Modelling, Simulation, and Control Course description
- Course name in Norwegian
- Advanced Methods in Modelling, Simulation, and Control
- Study programme
-
Master's Programme in Applied Computer and Information TechnologyMaster's Programme in Applied Computer and Information Technology, Elective modules
- Weight
- 10.0 ECTS
- Year of study
- 2025/2026
- Curriculum
-
FALL 2025
- Schedule
- Programme description
- Course history
-
Introduction
The course covers several aspects of model-based control and estimation methods. The focus is on industrial applications, implementation, real life problems, and hands-on experience. The course gives an overview of state-of-the-art techniques, and provides students with tools to analyse and solve further industrial and research problems. Strong emphasis is given to the use of numerical simulation and scientific programming with Matlab/Simulink or similar.
Recommended preliminary courses
- The Python programming language
- Scientific Programming using Python
- Automating tasks using Python
- Git
Required preliminary courses
This course covers the use of scripting as a programming paradigm to solve challenges like automation, integration, data manipulation and analysis. The focus is on understanding how scripting combined with utility libraries can be helpful in solving a task. Scripts can vary in length and complexity, but are normally written in a high-level language that focuses on ease of expression and readability as well as a powerful set of libraries for complex operations. Scripts can be written as a means to create tools that eases scientific work or automates tasks. They can also be used to make systems interact that would normally not. The course will use the Python programming language.
Learning outcomes
No formal requirements over and above the admission requirements.
Content
- Systematic approach to dynamic modeling
- Linearization of nonlinear models
- Data driven dynamic modelling (system identification)
- State estimation
- Model-based PID-tuning methods
- Multivariable control methods
- Predictive control algorithms
- Implementation, testing and validation with numerical simulation.
Teaching and learning methods
The student should have the following outcomes upon completing the course:
Knowledge
Upon successful completion of the course, the student:
- has a deep understanding of how scripting with Python is utilized to automate common tasks
- has advanced knowledge of scripting strategies that allow scripts to be robust against unforeseen failures and erroneous user input
- has advanced knowledge of how a code-base can be maintained through version control systems
- understands how scripting languages can be expanded through libraries
- knows how to use standardized packages for mathematics and statistics
Skills
Upon successful completion of the course, the student can:
- design and implement script-based tools
- evaluate and discuss how scripting may or may not facilitate automation
- use standard mathematics and statistics packages to visualize and solve relevant problems
- utilize a version control system for their code-base
General competence
Upon successful completion of the course, the student can:
- analyze automation approaches with regard to robustness and in relation to the intended tasks
- develop solution strategies for and participate in discussions about mathematical and statistical problems using scripting tools
- explain how automation and scripting can be used to automate workflows to experts and non-experts alike
Course requirements
This course is divided into two parts. The first part with focus on covering the particular scripting language used in this class, such as its syntax, use and some extra libraries. The first part will also cover the practice of using a version control system as the means to store the code-base. During this part, students will meet for weekly lectures/sessions and labs where they work on exercises.
The second part will focus on the students completing a programming project. The student will work individually on the project and submit a final code-base that also includes documentation. During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.
Practical training
Lab sessions.
Assessment
The following required coursework must be approved before the student can take the exam:
2 mandatory coding assignments, either done individually or in groups (max. 5 students).
Permitted exam materials and equipment
An individual project report between 5000 and 10000 words, not counting code appendix.
The exam can be appealed.
New/postponed exam
In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.
Grading scale
All aids are permitted, provided the rules for plagiarism and source referencing are complied with.
Examiners
Grade scale A-F.
Course contact person
One internal examiner. External examiners are used periodically.