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KTH / Teknikvetenskap / Matematik / Optimeringslära och systemteori

SF2852 Optimal Control,   2018, 7.5hp.



Course registration: If you have problems registering for the course or for exams, please contact the Student affairs office , e.g., via email: studentoffice@math.kth.se.

Some material from the lectures can be found on this page

Course home page address:
http://www.math.kth.se/optsyst/grundutbildning/kurser/SF2852/.

Examiner and lecturer:
Johan Karlsson, email: johan.karlsson@math.kth.se,
room 3550, Lindstedtsv 25, phone: 790 8440

Tutorial exercises:
Silun Zhang, silunz@kth.se,
room 3736, Lindstedtsv 25, phone: 790 6294.

Introduction
Optimal control is the problem of determining the control function for a dynamical system to minimize a performance index. The subject has its roots in the calculus of variations but it evolved to an independent branch of applied mathematics and engineering in the 1950s. The rapid development of the subject during this period was due to two factors. The first are two key innovations, namely the maximum principle by L. S. Pontryagin and the dynamic programming principle by R. Bellman. The second was the space race and the introduction of the digital computer, which led to the development of numerical algorithms for the solution of optimal control problems. The field of optimal control is still very active and it continues to find new applications in diverse areas such as robotics, finance, economics, and biology.

Course goals
The goal of the course is to provide an understanding of the main results in optimal control and how they are used in various applications in engineering, economics, logistics, and biology. After the course you should be able to

  • 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. You should also 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.

For the highest grade you should be able to integrate the tools you have learnt during the course and apply them to more complex problems. In particular you should be able to

  • explain how PMP and DynP relates to each other and know their respective advantages and disadvantages. In particular, you should be able to describe the difference between feedback control versus open loop control and you should use be able to compare PMP and DynP with respect to computational complexity.
  • explain the mathematical methods used to derive the results and combine them to derive the solution to variations of the problems studied in the course.
Course topics
  • Dynamic Programming Discrete dynamic programming, principle of optimality, Hamilton-Jacobi-Bellman equation, verification theorem.
  • Pontryagin minimum principle Several versions of Pontryagin Minimum Principle (PMP) will be discussed.
  • Infinite Horizon Optimal Control Optimal control over an infinite time horizon, stability, LQ optimal control.
  • Model Predictive Control Explicit and implicit model predictive control.
  • Applications Examples from economics, logistics, aeronautics, and robotics will be discussed.
  • Computational Algorithms The most common methods for numerical solution of optimal control problems are presented.

    Course material
    The required course material consists of the following lecture and exercise notes on sale at Kårbokhandeln. [Lecture notes], [Exercise notes].

    • Ulf Jönsson et. al. Optimal Control, Lecture notes, KTH.
    • Peter Ögren et. al. Exercise Notes on Optimal Control , KTH.
    • Supplementary material will be handed out during the course.

    Prerequisites
    The student is required to have passed the course optimization SF1841 or a course with similar content. The student should hence be familiar with concepts and theory for optimization: linear, quadratic, and nonlinear optimization; optimality conditions, lagrangian relaxation and duality theory. Familiarity with systems theory and state space is not required but recommended.

    Course requirements
    The course requirements consist of an obligatory final written examination. There are also three optional homework sets that we strongly encourage you to do. The homework sets give you bonus credits in the examination.

    PhD course SF3852
    It is possible to read this course as a PhD level course. For this, an extra project and at least a B on the exam is required. If you are interested in this option, email me that you would like to take this course and include a project that you are interested in working on (at the latest September 17).

    Homework sets
    Homework set 0: This homework set provides some review of systems theory and optimization as well as a Matlab exercise that use the toolbox CVX. I recommend that everyone does problem 2. Homework set 0 does not give bonus points to the exam, however, you can get feedback on your solutions if you hand it in before the deadline. Each of the homework sets 1-3 consists of three-five problems. The first two-three problems are methodology problems where you practice on the topics of the course and apply them to examples. Among the last two problems, one will focus on more theoretical nature and helps you to understand the mathematics behind the course. It can, for example, be to derive an extension of a result in the course or to provide an alternative proof of a result in the course. The other will focus on implementation and the student is required to make a Matlab program that solve a problem numerically.
    Each successfully completed homework set gives you maximally 2 bonus points for the exam. The bonus is only valid during the year it is acquired. The exact requirements will be posted on each separate homework set. The homework sets will be posted on the homepage roughly two weeks before the deadline. You may email the solutions to the homework. If you choose to do so, the solution should be submitted as one pdf prepared in LaTeX or comparable software.

    • Homework 0: This homework set covers some basic systems theory and optimization. (Due September 4, at 10.14). Here is the first homework set: [pdf].
    • Homework 1: This homework set covers problems on discrete dynamic programming and model predictive control. (Due on September 13, at 08.14). Here is the first homework set: [pdf].
    • Homework 2: This homework covers computational methods for solving optimal control problems. Random groups will be assigned for this project. Please sign up on [Form] by September 11 for groups. A report should be handed in October 2. Results should be discussed with fellow students on Thursday October 3, at 13.15. Attendance is mandatory for receiving the bonus points. [pdf]
    • Homework 3: This homework set covers problems on PMP and related topics (Due on October 10, at 13.14). [pdf]

    Matlab code

    Here are some Matlab routines that are used in the excerise notes. You may use this for the solution of your homeworks.

    Written exam
    You may use Beta Mathematics Handbook and the following formula sheet (pdf) . The exam will consist of five problems that give maximally 50 points. These problems will be similar to those in the homework assignments and the tutorial exercises. The preliminary grade levels are distributed according to the following rule, where the total score is the sum of your exam score and maximally 6 bonus points from the homework assignments (max credit is 56 points). These grade limits can only be modified to your advantage.

    Total credit (points) Grade
    45-56 A
    39-44 B
    33-38 C
    28-32 D
    25-27 E
    23-24 FX
    The grade FX means that you are allowed to make an appeal, see below.

    • [info, room, etc.].
    • You need to register for the exam. Information on how to register for the exam can be found here.

    Appeal
    If your total score (exam score + maximum 6 bonus points from the homework assignments and the computational exercises) is in the range 23-24 points then you are allowed to do a complementary examination for grade E. In the complementary examination you will be asked to solve two problems on your own. The solutions should be handed in to the examiner in written form and you must be able to defend your solutions in an oral examination. Contact the examiner no later than three weeks after the final exam if you want to do a complementary exam.

    Course evaluation
    At the end of the course you will be asked to complete a course evaluation form online.



    Schedule for 2018
    Type Date Time Room Topic Content
    L1 2018-08-27 13 E51 Introduction
    Discrete dynamic programming
    Introduction slides
    Lecture notes page 17-23
    L2 2018-08-28 10 E51 Discrete dynamic programming
    Discrete PMP
    Knapsack problem
    LQ problem. Pages 22-24
    E1 2018-08-29 13 V34 Discrete dynamic programming
    Linear systems
    L3 2018-08-30 8 E31 Discrete dynamic programming
    Infinite time horizon
    Pages 24-26, 32
    L4 2018-09-03 13 E51 Model predictive control MPC article
    (handed out in class)
    E2 2018-09-04 10 Q34 Model predictive control

    L5 2018-09-05 13 E51 Dynamic programming

    E3 2018-09-06 8 E51 Dynamic programming

    L6 2018-09-10 13 Q33 Dynamic programming
    and review
    L7 2018-09-11 10 Q36 Mathematical preliminaries
    (ODE theory etc)
    L8 2018-09-12 13 Q36 Pontryagins minimum principle
    (PMP) (using small variations)
    E4 2018-09-13 8 E51 PMP I

    L9 2018-09-17 13 D34 PMP (control constraints)

    L10 2018-09-18 10 Q36 PMP (optimal control
    to a manifold)
    E5 2018-09-19 13 E51 PMP II:
    Time optimal control
    L11 2018-09-24 13 E51 PMP (generalizations)

    E6 2018-09-25 10 V33 PMP III

    L12 2018-09-27 8 E51 PMP applications

    L13 2018-10-01 13 E51 Topics: Infinite time horizon
    optimal control
    E7 2018-10-02 10 Q33 PMP IV

    L14 2018-10-03 13 E51 Computational methods Seminar
    (student presentation)
    L15 2018-10-09 10 E51 Topics: Infinite time horizon
    optimal control
    E8 2018-10-10 13 V22 Infinite time horizon optimal control and
    Review: old exams
    L16 2018-10-11 8 Q21 Review

    Exam 2018-10-26 8 Exam

    Welcome!


    Last years exams can be found here:
    2018
    exam and solutions
    2017
    exam and solutions
    exam and solutions
    2016
    exam and solutions
    exam and solutions
    2015
    exam and solutions
    2014
    exam and solutions
    2013
    exam a
    solutions a
    2012
    exam b
    solutions b
    exam a
    solutions a
    2011
    exam b
    solutions b
    exam a
    solutions a