# Blogs by Tags

## Linear Control Theory: Part 0

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The purpose of this post is to introduce you to some of the basics of control theory and to introduce the Linear-Quadratic Regulator, an extremely good hammer for solving stabilization problems.

To start with, what do we mean by a control problem? We mean that we have some system with dynamics described by an equation of the form

$\dot{x} = Ax,$

where $x$ is the state of the system and $A$ is some matrix (which itself is allowed to depend on $x$). For example, we could have an object that is constrained to move in a line along a frictionless surface. In this case, the system dynamics would be

$\left[ \begin{array}{c} \dot{q} \\ \ddot{q} \end{array} \right] = \left[ \begin{array}{cc} 0 & 1 \\ 0 & 0 \end{array} \right]\left[ \begin{array}{c} q \\ \dot{q} \end{array} \right].$

## Robotics

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This summer I am working in the Robotics Locomotion group at CSAIL (MIT’s Computer Science and Artificial Intelligence Laboratory). I’ve decided to start a blog to exposit on the ideas involved. This ranges from big theoretical ideas (like general system identification techniques) to problem-specific ideas (specific learning strategies for the system we’re interested in) to useful information on using computational tools (how to make MATLAB’s ode45 do what you want it to).

To start with, I’m going to describe the problem that I’m working on, together with John (a grad student in mechanical engineering).

Last spring, I took 6.832 (Underactuated Robotics) at MIT. In that class, we learned multiple incredibly powerful techniques for nonlinear control. After taking it, I was more or less convinced that we could solve, at least off-line, pretty much any control problem once it was posed properly. After coming to the Locomotion group, I realized that this wasn’t quite right. What is actually true is that we can solve any control problem where we have a good model and a reasonable objective function (we can also run into problems in high dimensions, but even there you can make progress if the objective function is nice enough).

## The Underwater Cartpole

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My last few posts have been rather abstract. I thought I’d use this one to go into some details about the actual system we’re working with.

As I mentioned before, we are looking at a cart pole in a water tunnel. A cart pole is sometimes also called an inverted pendulum. Here is a diagram from wikipedia:

The parameter we have control over is F, the force on the cart. We would like to use this to control both the position of the cart and the angle of the pendulum. If the cart is standing still, the only two possible fixed points of the system are $\theta = 0$ (the bottom, or “downright”) and $\theta = \pi$ (the “upright”). Since $\theta = 0$ is easy to get to, we will be primarily interested with getting to $\theta = \pi$.

## Uncertain Observations

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What happens when you are uncertain about observations you made? For instance, you remember something happening, but you don’t remember who did it. Or you remember some fact you read on wikipedia, but you don’t know whether it said that hydrogen or helium was used in some chemical process.

How do we take this information into account in the context of Bayes’ rule? First, I’d like to note that there are different ways something could be uncertain. It could be that you observed X, but you don’t remember if it was in state A or state B. Or it could be that you think you observed X in state A, but you aren’t sure.

These are different because in the first case you don’t know whether to concentrate probability mass towards A or B, whereas in the second case you don’t know whether to concentrate probability mass at all.

Fortunately, both cases are pretty straightforward as long as you are careful about using Bayes’ rule. However, today I am going to focus on the latter case. In fact, I will restrict my attention to the following problem:

You have a coin that has some probability $\pi$ of coming up heads. You also know that all flips of this coin are independent. But you don’t know what $\pi$ is. However, you have observed this coin $n$ times in the past. But for each observation, you aren’t completely sure that this was the coin you were observing. In particular, you only assign a probability $r_i$ to your $i$th observation actually being about this coin. Given this, and the sequence of heads and tails you remember, what is your estimate of $\pi?$

## Linear Control Theory: Part I

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Last time I talked about linear control, I presented a Linear Quadratic Regulator as a general purpose hammer for solving linear control problems. In this post I’m going to explain why LQR by itself is not enough (even for nominally linear systems). (Author’s note: I got to the end of the post and realized I didn’t fulfill my promise in the previous sentence. So it’s redacted, but will hopefully be dealt with in a later post.) Then I’m going to do my best to introduce a lot of the standard ideas in linear control theory.

My motivation for this is that, even though these ideas have a reasonably nice theory from a mathematical standpoint, they are generally presented from an engineering standpoint. And although all of the math is right there, and I’m sure that professional control theorists understand it much better than I do, I found that I had to go to a lot of effort to synthesize a good mathematical explanation of the underlying theory.

However, this effort was not due to any inherent difficulties in the theory itself, but rather, like I said, a disconnect in the intuition of, and issues relevant to, an engineer versus a mathematician. I’m not going to claim that one way of thinking is better than the other, but my way of thinking, and I assume that of most of my audience, falls more in line with the mathematical viewpoint. What’s even better is that many of the techniques built up for control theory have interesting ramifications when considered as statements about vector spaces. I hope that you’ll find the exposition illuminating.

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