Course content

Module 1: Optimality conditions for constrained systems. Local/global conditions, Kuhn-Tucker conditions. Module 2: Optimizations algorithms (search algorithms) for LP, QP and nonlinear programs. Module 3: Optimal control, LQ-control. Focus is on discrete systems. Module 4: Model predictive control with industrial examples.

Learning outcome

Provide the students with knowledge on the use of optimization as a design tool. The knowledge is provided through theory and examples. Problems in static and dynamic systems will be addressed. Model predictive control is included in the latter.

Learning methods and activities

The course is given av a mixture of lectures, assignments and a laboratory project. 7 of the assignments and the laboratory report must be approved to enter the final exam. Portfolio evaluation is the basis for the final grade in the subject. Parts of the portfolio are final exam in writing 70%, project 15% and midterm test 15%. The result for each part is given in percentage units, while evaluation of the entire portfolio (the final grade) is given as a letter. If there is a re-sit examination, the examination form may change from written to oral.

Compulsory assignments

• Exercises

Recommended previous knowledge

Calculus 1, 2, 3 and 4 (TMA4100, TMA4105, TMA4115, TMA4120), TTK4105 Control Systems, TTK4115 Linear System Theory or a comparable background.

Course materials

Information on this is given at the start of the semester.

Credit reductions