Control of DiscreteTime Stochastic Systems
Course code: WI4217
Summary
Grade: 7.3
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 stateobserved 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: 7.3
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.
