The course provides detailed knowledge of developing static and dynamic models and related simulation software; strengthens programming skills; introduces data analysis techniques and data quality improvement methods; introduces control techniques, automation tools and automated systems; demonstrates laboratory for automated systems and realtime automation operations.
Adjustments to the plan may occur.
Students who successfully complete the course should achieve:
understand modeling procedures
know about data and data processing methods
know about control theories and observer design methods
know about automation tools and automated process
be able to perform basic programming in Matlab or Python
be able to process and analyze data
be able to implement control strategies for automated systems
be able to perform realtime operations through simulators
have a general understanding about high-level automated drilling techniques which also can be relevant to other areas
have insight into how automated laboratorial systems and software tools can be used for increased understanding and research
general programming competence which can be useful in different disciplines
Required prerequisite knowledge
Physics, mathematics, cybernetic engineering, mechanical engineering, drilling subjects and basic programming in e.g. Matlab or Python
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.