Control of Discrete-Time Stochastic Systems

Course code: WI4217

Summary Grade: 7.4

The files below summarize the parts of the reader that are treated in the lectures. (Although "summarize" might be the wrong word: sometimes the summary is longer than the original.)

  • Probability theory (102kb)
    Stochastic systems have uncertainties. To deal with them, we need probability theory. This file quickly discusses it.
  • Basics of stochastic systems (144kb)
    What are stochastic systems? And how do we represent them? We'll examine that here.
  • Properties of stochastic systems (91kb)
    In this chapter, we examine what properties stochastic systems can have.
  • Stochastic realizations (97kb)
    This chapter is about stochastic realizations. How do we find them? And how can we make sure that they are minimal?
  • Stochastic control (68kb)
    How do we control stochastic systems? This chapter discusses the types of control laws that can be used for it.
  • Dynamic programming (86kb)
    The most important method to find the optimal control law for a recursive state-observed system is dynamic programming. In this chapter, we’ll look at how it works.
  • Kalman filters (59kb)
    Kalman filters try to find the state of a system. But what kind of Kalman filters are there? And how do they work? This file examines it.
  • Control using partial observations (81kb)
    In this chapter we'll look at a really difficult problem: controlling a system of which we don't know the state. How does that work?
Full Version

Reader Measure Theoretic Probability Grade: 7.4

If you are having some trouble following the probability theory of this course, then the reader below might be useful. It comes from a Measure Theory course from another university.