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FSF3852 Optimal Control Theory 7.5 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FSF3852 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Dynamic programming in continuous and discrete time. Hamilton-Jacobi-Bellman equation. Theory of ordinary differential equations. The Pontryagin maximum principle. Linear quadratic optimization. Model predictive control. Infinite horizon optimal control problems. Sufficient conditions for optimality. Numerical methods for optimal control problems.

Intended learning outcomes

To pass the course, the student should be able to do the following:

  • Describe how the dynamic programming principle works (DynP) and apply it to discrete optimal control problems over finite and infinite time horizons.

  • Use continuous time dynamic programming and the associated Hamilton-Jacobi-Bellman equation to solve linear quadratic control problems.

  • Use the Pontryagin Minimum Principle (PMP) to solve optimal control problems with control and state constraints.

  • Use Model Predictive Control (MPC) to solve optimal control problems with control and state constraints. You should also be able understand the difference between the explicit and implicit MPC control and explain their respective advantages.

  • Formulate optimal control problems on standard form from specifications on dynamics, constraints and control objective. In addition be able to explain how various control objectives affect the optimal performance.

  • Explain the principles behind the most standard algorithms for numerical solution of optimal control problems and use Matlab to solve fairly simple but realistic problems.

  • Integrate the tools learnt during the course and apply them to more complex problems.

  • Explain how PMP and DynP relates to each other and know their respective advantages and disadvantages. In particular, be able to describe the difference between feedback control versus open loop control and also be able to compare PMP and DynP with respect to computational complexity.

  • Combine the mathematical methods used in optimal control to derive the solution to variations of the problems studied in the course.

Literature and preparations

Specific prerequisites

A Master degree including at least 30 university credits (hp) in in Mathematics (Calculus, Linear algebra, Differential equations and transform method), and further at least  6 hp in Mathematical Statistics, 6 hp in Numerical analysis and 6 hp in Optimization.

Recommended prerequisites

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Compendium from the department.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • TEN1 - Written exam, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Project, written examination, exercises.

Other requirements for final grade

Project, written examination.

Optional homeworks give bonus credits on written examination

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

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Johan Karlsson (

Postgraduate course

Postgraduate courses at SCI/Mathematics