TSRT08 · 6 credits · Advanced level (A1N) · Elektroteknik
Optimal Control
Active · next start HT 2026
82%
passed the latest ordinary exam
TEN1 · 14 Jan 2026 · 11 students
68%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 81 students
Unusually easy in 2025/26
82% passed the ordinary exam in 2025/26 (11 students), against 66% on average in earlier years (70 students).
Part of 9 programmes
- Master of Science in Computer Science and Engineering
- Master of Science in Industrial Engineering and Management - International
- Master of Science in Industrial Engineering and Management
- Master of Science in Information Technology
- Master of Science in Mechanical Engineering
- Master of Science in Engineering Mathematics
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Applied Physics and Electrical Engineering
- Master's Programme in Mathematics
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
68% passed · 81 students
- 82%11
- 67%15
- 65%23
- 59%17
- 73%15
Re-exams
No sittings with at least 5 students.
- Get 4 or 5: 28% (81 students)
Show small sittings (9)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 0%1
- 50%2
- 50%2
- 100%3
- 0%2
- 67%3
- 0%2
- 67%3
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Hand-in assignments
- G
Main rounds
100% passed · 83 students
- 100%11
- 100%17
- 100%22
- 100%17
- 100%16
Later rounds
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
About the course
Aim
Optimal control consists of theory and methods for controlling dynamical systems so that a mathematical criterion is minimized. The course aims at giving basic theory and engineering oriented computational methods in the area. After taking the course the participants are expected to be able to:
- Analyze and synthesize optimal open loop control signals using the Maximum principle.
- Analyze and synthesize optimal feedback laws using Dynamic Programming.
- Analyze and synthesize optimal feedback laws using Model Predictive Control.
- Analyze and synthesize optimal feedback laws using Reinforcement Learning.
- Use numerical software to solve optimal control problems.
Content
Formulation of the optimal control problem. The Maximum Principle. Optimal feedback and Dynamic Programming. Reinforcement Learning. Numerical methods for solving optimal control problems. Relations to MPC and convex optimization.
- Examiner
- Anders Hansson
- Scheduled hours
- 56 h
- Main field of study
- Elektroteknik