Credits: 5

Schedule: 07.01.2020 - 17.02.2020

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

Details on the course content (applies in this implementation): 

Actual course content (2019)

We study the mathematical principles of optimal control to manipulate the dynamic behaviour of process systems and the numerics used for its solution. The course aims at bringing understanding on how to combine numerical optimisation with dynamical systems theory to formulate and solve optimal control problems in both discrete- and continuous-time. We develop the topic in general application domains in chemical and bio-chemical engineering.

  • Introduction to dynamic process models and optimisation (Classes of dynamical process models; Classes of optimisation problems)
  • Numerical optimisation (Optimality conditions; Newton-type algorithms) and automatic differentiation;
  • Discrete-time optimal control (Formulation; Sparsity; Dynamic programming; Infinite-horizon problems; Iterative and differential);
  • Continuous-time optimal control (Formulation; Hamilton-Jacobi-Bellman and Pontraygin equations; Direct and indirect methods);
  • Online optimal control (Model-predictive control; Moving horizon control and estimation).

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