Course - Optimization and Control - TTK4135
Optimization and Control
Choose study yearAssessments and mandatory activities may be changed until September 20th.
About
About the course
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.
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
Course code | Reduction | From |
---|---|---|
SIE3030 | 7.5 sp |
Subject areas
- Technological subjects