ACIT4810 Advanced Methods in Modelling, Simulation, and Control Course description

The course covers several aspects related to modelling, simulation and control. 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.

None.

No formal requirements over and above the admission requirements.

Upon successful completion of the course, the student:

Knowledge:

• is familiar with data-driven dynamic modelling methods
• is familiar with state estimation techniques
• is familiar with multivariable feedback control algorithms
• is familiar with predictive control algorithms

Skills:

• can obtain dynamic models with data-driven dynamic modelling methods for a chosen industrial application
• can implement some techniques for state estimation
• can implement some multivariable feedback control algorithms
• can implement some predictive control algorithms
• can choose an advanced control algorithm suitable for a chosen industrial application.
• can implement and test the algorithm in a simulation environment.

General competence:

• can evaluate different methods for advanced control for a chosen industrial application.
• can choose a suitable method, develop and implement it for a chosen industrial application.

The final grade will be based on:

• Individual written/digital exam 3 hours duration 50%
• Individual or group (2 - 5 students) project work (15 - 30 pages) 50%

The percentage is an indication of relative weight. The assessment of the project takes the size of the group into account and a somewhat larger project report is expected with larger groups.

Both exams must be passed in order to pass the course.

Weekly lectures and exercises, one project work in groups of between 2 - 5 students. Two guest lectures on selected topics given by experts from industry and academia.

The following required coursework must be approved before the student can take the exam:

Four individual compulsory assignments and one group project. The project groups will be between 2 and 5 students. The resulting project report is about 15-25 pages.

Exam autumn 2020 due to Covit-19:

3 days written digital home exam.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In case an oral exam is used, the examination result cannot be appealed.

[Exam earlier]:

Written exam, 3 hours.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In case an oral exam is used, the examination result cannot be appealed.

Unmanned Aerial Vehicles (UAVs) are a disruptive technology that is revolutionizing data gathering, earth observation, environmental monitoring, mapping, and transport to name only a few. This course provides a hands-on overview of common theories and methods used in the design of aerial robotic systems. The course is organised around weekly practical labs and lectures that complement each other. The student will get hands-on experience with the technologies as well as a holistic perspective on the architecture of aerial robotic systems. The course uses examples from multirotor and fixed wing types of vehicles and focuses both on autonomous and remotely piloted aerial systems (RPAS).

No formal requirements over and above the admission requirements.

Knowledge

Upon successful completion of the course, the student should:

• have advanced knowledge on aerial robotic system components and architecture
• have advanced knowledge on rules and regulations regarding RPAS/UAS systems
• have advanced knowledge in modeling and simulation of aerial robotic systems
• have advanced knowledge in common sensors, actuators, communication devices, video transmission, and hardware component technologies used in aerial robots
• have deep knowledge of algorithms and methods used in navigation, guidance and control of aerial robots

Skills

Upon successful completion of the course, the student:

• can analyze aerial robotic systems with regard to its components, architecture, and their purpose
• can model, analyze, and simulate aerial robotic systems
• can apply a number of algorithms and methods in navigation, guidance, and control of aerial robots

General competence

Upon successful completion of the course, the student:

• can discuss the role of aerial robotic systems in a number of practical applications

This course will feature weekly lectures and lab work to provide both theoretical and hands- on experience. Students will work in groups and complete assignments given to them. The student will supplement the lectures and lab with their own reading. The students will also work on an individual or group project of between 2 - 5 students.