course-details-portlet

TTK4135 - Optimization and Control

About

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Programming assignments 20/100
School exam 80/100 4 hours D

Course content

The course subject is optimization. The candidates learn to formulate optimization problems and solve these through appropriate algorithms and software. Optimality conditions like the Karush-Kuhn-Tucker (KKT) conditions are discussed and conditions for global and local conditions are analyzed. Key optimization classes of problems including linear programming (LP), quadratic programming (QP) and nonlinear programming (NLP) are studied and applied in different settings. The course includes advanced control of dynamic systems with emphasis on Model Predictive Control (MPC).

Learning outcome

Knowledge: - Ability to formulate appropriate engineering problems as a mathematical optimization problem. - Knowledge of typical engineering problems which are suitable for optimization. - Ability to analyze and solve an optimization problem; in particular linear programs (LP), quadratic programs (QP) and nonlinear programs (NP). - Ability to analyze and design optimal controllers; in particular Model Predictive Controllers (MPC). - Knowledge of optimization software. Skills: - Solve suitable optimization problems using Matlab. - Use optimization in controllers; in particular Model Predictive Control (MPC). - Complete a small optimization project. - Analyze a problem and contribute to innovative design solutions. General competence: - Communicate technical issues with specialists in cross-disciplinary teams and the general public. - Conscious attitude towards the use of optimization within engineering.

Learning methods and activities

The course is given as a mixture of lectures, assignments, programming assessments and a laboratory project. Seven of the assignments and the laboratory report must be approved to enter the final exam.

Compulsory assignments

  • Exercises
  • Laboratory report

Further on evaluation

There are two partial assessments in the course. The first is based on completion of programming assignments, and counts 20%. The second is the final written (digital) exam, which counts 80%. Both partial assessments must be passed for the course to be passed.

There are two compulsory work requirements (7 out of 10 exercises, and report from laboratory work) that must be approved.

If there is a re-sit examination, the examination form may change from written to oral. The resit exam is in august.

When a non-passed course is re-taken, the compulsory work requirements and both partial assessments must be done again.

Course materials

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

Credit reductions

Course code Reduction From To
SIE3030 7.5
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III

Coursework

Term no.: 1
Teaching semester:  SPRING 2025

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Engineering Cybernetics

Timetable

List view
Calendar view

Examination

Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 80/100 D INSPERA
Room Building Number of candidates
Spring ORD Programming assignments 20/100 INSPERA
Room Building Number of candidates
Summer UTS School exam 80/100 D INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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