Course content

1. Modeling of dynamical systems
2. Numerical simulation
3. State and parameter estimation
4. Kalman filter
5. State observer
6. Fault diagnosis
7. Numerical optimization
8. Optimization of dynamic systems
9. Model predictive control

Learning outcome

Demonstrate knowledge about general and specific theories, challenges, algorithms, methods, technologies, and tools related to modelling, simulation, control, and optimization of dynamic systems.

Learning methods and activities

Lectures, tutorials, and assignments.

Further on evaluation

The portfolio contains assignments that are carried out, digitally documented and submitted during the term. Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio. The final grade is based on an overall evaluation of the portfolio, which consists of a number of work delivered through the semester.

Recommended previous knowledge

Courses related to modeling, simulation, optimization, and Control.

Course materials

Lecture notes and videos.