Credits: 5

Schedule: 08.01.2019 - 18.02.2019

Teacher in charge (valid 01.08.2018-31.07.2020): 

Sirkka-Liisa Jämsä-Jounela

Teaching Period (valid 01.08.2018-31.07.2020): 


Learning Outcomes (valid 01.08.2018-31.07.2020): 

After completing the course, the student*Understands the main principles of the model identification
*Is familiar with the identification toolbox
*Understands and is able to apply Kalman filtering for the state estimation
*Is familiar with the basics of multivariable control
*Knows the discrete time control and is able to formulate and solve dynamic models in discrete time
*Understands and is able to use Model Predictive Control (MPC)

Content (valid 01.08.2018-31.07.2020): 

The course includes the selected topics of advanced control theory: model identification, state estimation with Kalman filter, multivariable control, discrete time systems and design of digital controllers, model predictive control. The course is focused on multivariate systems.
Identification of the mixing tank
PI controller and decouplers design for the 3-tank system
MPC design + state estimation for the three-tank system
Experimental modelling of a distillation column
Estimation using a Kalman filter

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Independent study and exam

Workload (valid 01.08.2018-31.07.2020): 

Lectures 24 h
Exercises 24 h
Assignments + independent study 83 h
Exam 4 h

Study Material (valid 01.08.2018-31.07.2020): 

To be announced later.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

KE-90.4510 Control Applications in Process Industries (6 op), CHEM-E7145 Advanced Process Control Methods and Process Control Project Work (5 cr)

Grading Scale (valid 01.08.2018-31.07.2020): 

Fail, 1 - 5

Registration for Courses (valid 01.08.2018-31.07.2020): 



Registration and further information