Practice English Speaking&Listening with: Lec 5 | MIT 6.01SC Introduction to Electrical Engineering and Computer Science I, Spring 2011

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PROFESSOR: Last time, which is now two weeks ago, we started

the talk about the signals and systems approach, by which I

mean, think about a system by the way it transforms its

input signal into an output signal.

That's kind of a bizarre way of thinking about systems.

I demonstrated that last time by thinking about a mass and a

spring, something you have a lot of experience with, but

you probably didn't use this kind of approach for

thinking about it.

Over this lecture and over the next lecture, what I'd like to

do is show you the advantages of this kind of approach.

And so for today, what I'd like to do is talk about how

to think about feedback within this structure, and I'd like

to also think about how you can use this structure to

characterize the performance of a system in a

quantitative fashion.

So first off, I want to just think about feedback.

Feedback is so pervasive that you don't notice

it most of the time.

You use feedback in virtually everything that you do.

Here is a very simple example of driving a car.

If you want to keep the car in the center of your lane,

something that many people outside of Boston, at least,

think is a good idea, then you are mentally doing feedback.

You're constantly comparing where you are to where you'd

like to be and making small adjustments to the system

based on that.

When you think of even the most simple of systems, like

the thermostat in a house--

I'm not talking about a cheap motel.

The cheap motels don't do this.

But in a real house, there's a thermostat, which regulates

the temperature.

That's important, because if the temperature suddenly

drops, it would never do that of course, but if the

temperature ever dropped, it could compensate for it.

Here's one of my favorite examples.

Feedback is enormously pervasive in biology.

There's no general rules in biology, but to a first cut,

everything is regulated.

And in many cases, the regulation is amazing.

Here's an example from 6.021, where it's illustrating the

system that your body uses to regulate glucose delivery from

food sources to every cell in your body, which is crucial to

your being cognizant and mobile.

And the idea is that it does that with amazing precision

despite the fact that eating and exercise

are enormously episodic.

In order for you to remain healthy and functional, you

need to have something between approximately two and ten

millimoles per liter of glucose in your

blood at all times.

Were it to go higher than that, systematically, you

would develop cardiac problems and could lead to even

congestive heart failure.

If you were to have lower than two millimoles per liter, you

would go comatose.

That's a very narrow range, two to five, especially

because what we eat is so episodic, when we exercise is

so episodic.

It's amazing, and just to dramatize how amazing it is,

how much sugar do you think circulates in

your blood right now?

Well, if you convert five millimoles per liter, and if

you assume the average person has about three liters of

blood, which is true, and if you calculate it, that comes

out to 2.7 grams.

That's this much.

This is 2.7 grams of table sugar.

So this is how much sugar is in your blood, now.

This is what's keeping you healthy.

This is what's keeping you from becoming comatose.

Or not.

How much sugar do you think is in this?

AUDIENCE: The amount in that other cup.


We call that the Theory of Lectures.

I don't want to look like an idiot, so my

problems make sense.

This is the amount of sugar in a can of soda.

Numerically, this is 39 grams.

That's more than 13 times this much.

So when you down one of these, it's very important that the

sugar gets taken out of the blood quickly, and it does.

And that happens by way of a feedback system, and the

feedback system is illustrated here.

Basically, the feedback involves the hormone insulin,

and that's why insulin deficiency is such a

devastating disease.

Finally, everything we do has feedback in it.

Think about how your life would be

different if it didn't.

Even a simple task, like removing a light bulb, would

be virtually impossible except for the fact that you get

feedback from everything.

Your hand's amazing.

You have touch sensors, you have proprioceptive sensors,

you have stress sensors on the muscles and ligaments, and

they all coordinate to tell you when to stop squeezing on

the light bulb so you don't break it.

That's all completely amazing, and what we'd like to do,

then, is think about that kind of a system, a feedback

system, within the signals and systems construct.

As an example, I want to think through the WallFinder problem

that you did last week in Design Lab.

I'm sure you all remember that, in that problem, we were

trying to move the robot to a fixed distance away from the

wall, and we thought about that as a feedback system

comprised of three parts, a controller, a

plant, and a sensor.

We wrote difference equations to characterize each of those

parts, and then we figured out how to solve those difference

equations to make some meaningful prediction about

how the robot would work.

So just to make sure you're all on board with me, now,

here's a question for you.

These are the equations that describe

the WallFinder problem.

How many equations and how many unknowns are there?

Take 20 seconds, talk to your neighbor, figure out an answer

between (1) and (5).

Unlike during the exam next week, you are allowed to talk.


PROFESSOR: T and K are no.


PROFESSOR: OK, so what's the answer, number (1), (2), (3),

(4), or (5)?

Everybody raise your hands.

Let me see if I got the answer.


Come on, raise your hands.

Come on, everybody vote.

If you're wrong, just blame it on your neighbor.

You had a poor partner.

That's the idea.


So you can all vote, and you don't need to worry about

being wrong.

And you're all wrong, so that worked out well.

OK, so I don't like the-- so the predominant answer is

number (2).

I don't like number (2).

Can somebody think of a reason, now that you know the

answer by the Theory of Lectures, the

answer is not (2)?

Why isn't the answer (2)?


AUDIENCE: Oh, I was saying (5).

PROFESSOR: You were saying (5).

Why did you say (5)?

AUDIENCE: I don't remember for sure, but can you substitute n

for different things?

Like Df of n, you could substitute n for, and if you

did that you would have two equations, which is


PROFESSOR: [UNINTELLIGIBLE] kind of thing, you count

before or after doing substitution and


And I mean to count before you do simplification.

Any other issues?



PROFESSOR: Say again?


PROFESSOR: Is D0_n different or the same from D0_n-1?

That's the key question.

So I want to think about this as a system of algebraic

equations, and if I do that, then there's a lot of them.

So it looks like there's three equations.

The problem with that approach is that there's actually three

equations for every value of n.

If you think about a system of equations that you could solve

with an algebra solver, you would have to treat all the

n's separately.

That's what we call the samples approach.

So here's a way you could solve them.

You could think about what if k and t are parameters, so

they're known, they're given?

What if my input signal is known, say it's a unit sample

signal, for example?

What would I need to solve this system?

Well, I'd need to tell you the initial conditions.

So in some sense, I want to consider those to be known.

So my knowns kind of comprise t and k, the initial

conditions for the output of the robot, and the sensor, all

of the input signals because I'm telling you the input and

asking you to calculate the output.

My unknowns are all of the different velocities for all

values of n bigger than or equal to 0 because I didn't

tell you those.

All of the values of robot's output at samples

n bigger than 0.

All the values of the sensor output for values

n bigger than 0.

So I get a lot of unknowns, infinitely many, and I get a

lot of equations, also infinitely many.

So the thing I want you think about is, if you're thinking

about solving difference equations using algebra,

that's a big system of equations.

By contrast, what if you were to try to solve the system

using operators?

Now, how many equations and unknowns do you see?

By the Theory of Lectures.

OK, raise your hand.

Or talk to your neighbor so you can blame your neighbor.

Talk to your neighbor.

Get a good alibi.


PROFESSOR: So the idea here is that if you think about

operators instead, so that you look at a whole signal at a

time, then each equation only specifies one relationship

among signals, and there's a small number of signals.

So if I think about the knowns being k, t the parameters and

the signal d(i), and if I think about the unknowns being

the velocity, the output, and the sensor signal, then I get

three equations and three unknowns.

So one of the values of thinking about the operator

approach is that it just simply reduces the amount of

things you need to think about.

It reduces complexity.

That's what we're trying to do in this course.

We're trying to think of methods that allow you to

solve problems by reducing complexity.

We would like, ultimately, to solve very complicated

problems, and this operator approach is an approach that

lets you do that.

It does a lot more than that, too.

It also generates new kinds of insights.

So it lets you focus on the relations, but the relation is

now not quite the same as we would have

expected from algebra.

Now, the relation between the input signal and the output

signal is an operator.

We're going to represent the operation that transforms the

input to the output by this symbol, h.

We'll call that the system functional.

It's an operator.

It's a thing that, when operated on x, gives you y.

And this is one of the main purposes of today's lecture,

it's also convenient to think about h as a ratio.

We like to think of it that way because, as we'll see,

there's a way of thinking about h as a ratio of

polynomials in R. So two ways of thinking about it.

We're trying to develop a signals and systems approach

for thinking about feedback.

We want to think about the input goes into a box.

The box represents an operation.

We will characterize that by a functional.

We'll call the functional h.

The functional, when applied to the input, produces the

output, and what we'd like to do is infer what is the nature

of that functional, and what are the properties of the

system that functional represents.

OK, so I see that you're all with me.

Think about the WallFinder system.

Think about the equations for the various components of that

system when expressed an operator form.

And figure out the system functional for that system.

figure out the ratio of polynomials that can be in r

that is represented by h.

Take 30 seconds, talk to your neighbor, figure out whether

the answer is (1), (2), (3), (4), or (5).

So what's the answer -- (1), (2), (3), (4), or (5)?

Come on, more voter participation.

All right?

Blame it on your partner.

OK, virtually 100% correct.

So the idea is algebra.

You solve the operator equations exactly as though

they were algebraic.

Here, I've started with the second equation and just done

substitutions until I got rid of everything other

than d(0) and d(i).

So I express v in terms of ke, then I express e in terms of

d(i) minus rd(0).

Then I'm left with one equation that relates d(0) and

d(i), which I can solve for the ratio.

And the answer comes out there, which was number (3).

Point is that you can treat the operator just as though it

were algebra, so that results in enormous implications.

But what we want to understand is what's the relationship

between that functional, that thing that we just calculated,

and the behaviors.

These are the kinds of behaviors that you observed

with the WallFinder system.

When you built the WallFinder system, depending on what you

made k, you could get behaviors that were monotonic

and slow, faster and oscillatory, or even faster

and even more oscillatory.

And what we'd like to know is, before we build it, how should

we have constructed the system so that it

has a desirable behavior?

And, incidentally, are these the best you can do?

Or is there some other set of parameters that's lurking

behind some door that we just don't know about, and if we

could discover it, it would work a lot better?

So the question is, given the structure of our problem,

what's the most general kind of answer that we can expect?

And how do we choose the best behavior out of that set of

possible behaviors?

So that's what I want to think about for the rest of the hour

and a half, and I want to begin by taking a step

backwards and look at something simpler.

The idea's going to be the same as the idea that we used

when we studied Python.

I want to find simple behaviors, think about that as

a primitive, and combine primitives to get a more

complicated behavior, so I want to use an approach that's

very much PCAP.

Find the most simple behavior, and then see if I can leverage

that simple behavior to somehow understand more

complicated things.

So let's think about this very simple system that has a

feedback loop that has a delay in it and a gain of P0.

What I want to do is think about what would be the

response of that very simple system if the input were a

unit sample.

So find y, given that the input x is delta.

In order to do that, I have to start the system somehow.

I will start it at rest.

You've all seen already, I'm sure, that rest is the

simplest assumption I can make.

I'll say something at the end of the hour about how you deal

with things that are not at rest.

For the time being, we'll just assume rest

because that's simple.

Assume that the system is at rest, that means that the

output of every delay box is 0.

That's what rest means.

If the output of this starts at 0, then the output of the

scale by P0 is also 0.

And if that's 0, and if the input is at 0 because I'm at

time before 0, then the output is 0, indicated here.

So now if I step, then the input becomes 1 because

delta of 0 is 1.

The output of the delay box is still 0, so the first answer

is 1, the 1 just propagates straight through


Then I step, and the 1 that was here goes through the r

and becomes 1, the 1 goes through P0 and becomes P0, but

at the same time, the 1 that was at the input goes to 0

because the input is 1 only at time equals 0.

So the result, then, is that after one step, the

output has become P0.

This propagated to 1, that became P0, add it to 0, and it

became P0, so now the answer, which had been 1, is P0.

On the next step, a very similar thing happens.

The P0 that was here becomes the output of the delay, gets

multiplied by P0 to give you P0 squared, gets added to 0 to

give you P0 squared, et cetera.

The thing that I want you to see is that the output was in

some sense simple.

The value simply increased as P0 to the n, geometrically.

There's another way we can think about that.

I just did the sample by sample approach, but the whole

theme of this part of the course

is the signals approach.

If I think about the whole signal in one fell swoop, then

I can develop an operator expression to

characterize the system.

The operator expression says the signal y is constructed by

adding the signal x to the signal P0RY.

If I solve that for the ratio of the output to input, I get

1 over (1 minus P0R).

Again, going back to the idea that I started with, that

we're going to get ratios of polynomials in R now the R is

in the bottom, and now I can expand that just as though it

were an algebraic expression.

I can expand 1 over P0R in a power series by using

synthetic division.

The result is very similar in structure to the result we saw

in sample by sample.

It consists of an ascending series in R, which means an

ascending number of delays.

Every time you increase the number of delays by 1, you

also multiply the amplitude by P0, so this is, in fact, the

same kind of result, but viewed from a

signal point of view.

Finally, I want to think about it in terms of block diagrams.

Same idea, I've got the same feedback system, but now I

want to take advantage of this ascending series expansion

that I did and think about each of the terms in that

series as a signal flow path through the feedback system.

So one, the first term in the ascending series, represents

the path that goes directly from the input to the output,

passing through no delays.

The second term in the series, P0R, represents the path that

goes to the output, loops around, comes back through the

adder, and then comes out.

In traversing that more complicated path, you picked

up 1 delay and 1 multiply by P0.

Second term, two loops.

Third term, three loops.

Fourth term, four loops.

The idea is that the block diagram gives us a way to

visualize how the answer came about.

It came about by all the possible paths that lead from

the input to the output.

Those possible paths all differed by a delay, and

that's why the decomposition was so simple, each path

corresponding to a different number of

delays through the system.

That won't always be true from more complicated systems, but

it is true for this one.

This flow diagram also lets you see something that's

extremely interesting.

Cyclical flow paths, which are characteristic of feedback--

feedback means the signal comes back.

Cyclical flow paths require that transient inputs generate

persistent outputs.

They generate persistent outputs because the output at

time n is not triggered by the input at time n.

It's triggered by the output at time n minus 1.

It keeps going on itself.

That's fundamental to feedback.

There's no way of getting around that.

That's what feedback is.

And it also shows why you got that funny oscillatory

behavior in WallFinder.

There wasn't any way around that.

Feedback meant that you were looping back.

That meant that there was a cycle in

the signal flow paths.

That means that even transient signals, signals that go away

very quickly like the [INAUDIBLE] sample, generate

responses that go on forever.

So that's a fundamental way of thinking about systems.

Systems are either feedforward or feedback.

Feedforward means that there are no cyclic

paths in the system.

No path in the system that take you from the input to the

output has a cycle in it.

That's what acyclic means.

That's what feedforward means.

Acyclic, feedforward, those all have responses to

transient inputs that are transient.

That contrasts with cyclic systems.

A cyclic system has feedback and will have the property

that transient signals can generate

outputs that go on forever.

OK, how many of these systems are cyclic?

Easy questions.

15 seconds, talk to your neighbor.

OK, so what's the answer?

How many?

OK, virtually 100%.

Correct, the answer's (3).

I've illustrated the cycles in red, so there's a cycle here,

there's two cycles in this one, and there's a cycle here.

So the idea is that, when you see a block diagram, one of

the first things you want to characterize, because it's

such a big difference between systems, is whether or not

there's a cycle in it.

If there's a cycle, then you know there's feedback.

If there's feedback, then you know you have the potential to

have a persistent response to even a transient signal.

OK, so if you only have one loop of the type that I

started with, where we had just one loop with an R and a

P0, then the question is, as you go around the loop, do the

samples get bigger, or smaller, or do

they stay the same?

That's a fundamental characterization of how the

simple feedback system works.

So here, if on every cycle the amplitude of the signal

diminishes by multiplication by half, that means that the

response ultimately decays.

Mathematically, it goes on forever, just like I said

previously, but the amplitude is decaying, so practically it

stops after a while.

It becomes small enough that you lose track of it.

By contrast, if every time you go around the loop, you pick

up amplitude, if the amplitude here were multiplied by 1.2,

then it gets bigger.

So the idea, then, is that you can characterize this kind of

a feedback by one number.

We call that number the pole.

Very mysterious word.

I won't go into the origins of the word.

For our purposes, it just simply means the base of the

geometric sequence that characterizes the response of

a system to the unit sample signal.

So here, I've showed an illustration of what can

happen if p is 1/2, p is one, p is 1.2, which you can see

decay, persistence, divergence.

Can you characterize this system by P0?

And if so, what is P0?




PROFESSOR: Yeah, and virtually everybody's

getting the right answer.

The right answer's (2).

So we like algebra.

We like negative numbers, so we're allowed to think about

poles being negative.

In fact, by the end of the hour, we'll even think about

poles having imaginary parts, but for the time

being, this is fine.

If the pole were negative, what that means is the

consecutive terms in the unit sample response, the response

of the system to a unit sample signal, the unit sample

response, the unit sample response can

alternate in sine.

OK, so this then represents all the possible behaviors

that you could get from a feedback system

with a single pole.

If a feedback system has a single pole, the only

behaviors that you can get are represented

by these three cartoons.

So here, this z-axis contains all possible values of P0.

If P0 is bigger than 1, then the magnitude diverges, and

the signal grows monotonically.

If the pole is between 0 and 1, the response is also

monotonic, but now it converges towards 0.

If you flip the sign, the relations are still the same,

except that you now get sign alternation.

So if the P0 is between 0 and minus 1, which is here, the

output still converges because the magnitude of the pole is

less than 1.

But now the sign flips.

And if the pole is below minus 1, then you get alternation,

but you also get divergence.

The important thing is we started with a simple system,

and we ended up with an absolutely complete

characterization of it.

This is everything that can happen.

That's a powerful statement.

When I can analyze a system, even if it's simple, and find

all the possible behaviors, I have something.

If you have a simple system with a single pole, this is

all that can happen.

There might be offsets.

There might be delays.

The signal may not start until the fifth sample, but the

persistent signal will either grow without bounds, the k to

0, or do one of those two with alternating sign.

That's the only things that can happen, which of course,

begs the question, well, what if the system's more


OK, so here's a more complicated system.

This system cannot be represented by just one pole.

In fact, the system's complicated enough you should

think through how you would solve it.

You should all be very comfortable with

this sort of thing.

So if you were to think about what if I had a system like

so, and I want it to be 1.6 minus 0.63.

What would be the output signal at time 2 if the input

were a unit sample signal?

OK, as with all systems we're going to think about, we have

to specify initial conditions.

The simplest kind of initial conditions we could think

about would be rest.

If I thought about this system at rest, then the initial

outputs of the R's would be 0.

That's at rest.

For times less than 0, the input would be 0.

0 times 1.6 plus 0 times -0.63 plus 0 would give me 0.

Now, the clock ticks.

When the clock ticks, it becomes times 0.

At times 0, the input is 1.

This 0 just propagated down to here, but this was 0, so

nothing interesting happens at the R's.

But now my output is 1.

Now, the clock ticks.

What happens?

When the clock ticks, this 1 propagates down here.

This 0 propagates down here, but that was 0.

This 1 goes to 0 because the input's only 1 at times 0.

So what's the output?


Now, the clock ticks.

What happens?

Well, this 1 comes down here.

This 1.6 comes down here.

This 0 becomes another 0 because the input has an

infinite stream of 0's after the initial time.

So what's the output?

Well, it's 1.6 times 1.6 plus 1 times -0.63, so the answer

is number (3).



I forgot to write it up there, so the

answer's in red down here.

1.6 squared minus 0.63.


The point is that it's slightly more complicated to

think about than the case with a single pole, and in fact, if

you use that logic to simply step through all the

responses, you get a response that doesn't look geometric.

The geometric sequences that we looked at previously either

monotonically increased, monotonically decreased

towards 0, or give one of those two things and


This does none of those behaviors.

So the point is Freeman's an idiot.

He spent all that time telling us what one pole does, and now

two poles does something completely different.


So the response is not geometric.

The response grows and then decays.

It never changes sign.

It does something completely different from what we would

have expected from a single pole system.

As you might expect from the Theory of Lectures, that's not

the end of the story.

So the idea is to now capitalize on this notion that

we can think about operators as algebra.

If our expressions behaved like I told you they did last

lecture, if they behaved as entities upon which--

if they are isomorphic with polynomials, as I said, then

there's a very cute thing we can do with this system to

make it a lot simpler.

The thing we can do is factor.

If we think about the operator expression to characterize

this system, the thing that's different is that

there's an R squared.

But if R operators work just like polynomials--

you can factor polynomials.

That's the factor theorem from algebra.

And if I factor it, I get two things that look like

first-order systems.

Well, that's good.

The factored form means that I can think about this more

complicated system as the cascade of

two first-order systems.

Well, that's pretty good.

In fact, it doesn't even matter what order I put them

in because, as we've seen previously, if the system

started at initial rest, then you can swap things because

they obey all the principles of polynomials, which include


So what we've done, then, is transform this more

complicated system into the cascade of two simple systems,

and that's very good.

Even better, we can think about the complicated system

as the sum of simpler parts, and that uses more intuition

from polynomials.

If we have one over a second-order polynomial, we

can write it in a factored form here, but we can expand

it in what we call partial fractions.

We can expand this thing in this sum, and if you think

about putting this over a common denominator and working

out the relationship, this difference, 4.5 over 1 minus

0.9R minus 3.5 over 1 minus 0.7R.

That's precisely the same using the normal rules for


That's precisely the same as that expression.

But the difference, from the point of view of thinking

about systems, is enormous.

We know the answer to that one.

That's the sum of the responses to two first-order

systems, so we can write that symbolically this way.

We can think about having a sum system that

generates this term.

This term is a simple system of the type of that we looked

at previously that, then, gets multiplied by 4.5.

I'm just factoring again.

I'm saying I've got something over something, which means

that I can put something in each of two different parts of

two things that I multiply together.

And I can think about this as having been generated by this

system, and you just add them together.

The amazing thing is that that says that, despite the fact

that the response looked complicated, it was in fact

the sum of two geometrics.

So it wasn't very different from the answer

for a single pole.

What I've just done is amazing.

I've just taken something that, had you studied the

difference equations and had you studied the block

diagrams, it would have been very hard for you to conclude

that something this complicated has a response

that can be written as the sum of two geometrics.

By thinking about the system as a polynomial in R, it's

completely trivial.

It's a simple application of the rules for polynomials that

you all know.

So what we've shown, then, is that this complicated system

has a way of thinking about as just two

of the simpler systems.

The complicated response that grew and decayed, that's just

the difference, really, 4.5 minus 3.5.

It's the weighted difference of a part that goes 0.7 to the

n, then a different part that goes 0.9 to the n.

So far, we've got to results, the n equals 1 case, the

first-order polynomial in our case, the one pole case,

that's trivial.

It's just a geometric sequence.

The response is just a geometric sequence.

If it happens to be second-order, this is

second-order because when you write the operator expression,

the polynomial in the bottom is second-order, second-order

polynomial in R.

This second-order system has a response that

looks like two pieces.

Each piece looks like a piece that was from

a first-order system.

And in fact, that idea generalizes.

If we have a system that can be represented by linear

difference equation with constant coefficients that

will always be true if the system was constructed out of

the parts that we talked about, adders, gains, delays.

If the system is constructed out of adders, gains, and

delays, then it will be possible to express the system

in terms of one difference equation.

General form is showed here.

Y then can be constructed out of parts that are delayed

versions of Y and delayed versions of X. If you do that,

then you can always write the operator that expresses the

ratio between the output and the input as the ratio of two


That will always be true.

So this, now, is the generalization step.

We did the n equals 1 case, we did the n equals 2 case, and

now we're generalizing.

We will always get, for any system that can be represented

by a linear difference equation with constant

coefficients, we can always represent the system

functional in this form.

Then just like we did in the second-order case, we can use

the factor theorem to break this polynomial in the

denominator into factors.

That comes from the factor theorem in algebra.

Then we can re-express that in terms of partial fractions.

And what I've just showed is that, in the general case,

regardless of how many delays are in the system, if the

system only has adders, gains, and delays, I can always

express the answer as a sum of geometrics.

That's interesting.

That means that if I knew the bases for all of those

geometric sequences, I know something about the response.

The bases are things we call poles.

If you knew all the poles, you'd know something very

powerful about the system.

So every one of the factors corresponds to a pole, and by

partial fractions, you'll get one response for each pole.

The response for each pole goes like pole to the n.

You know the basic shape.

You don't know the constants, but you know the basic shape

of the response just by knowing the poles.

We can go one more step, which makes the

computation somewhat simpler.

I used the factor theorem.

Here, I'm using the fundamental theorem of

algebra, which says that if I have a polynomial of order n,

I have n roots.

The poles are related to the roots of the R polynomial.

The relationship is take the functional,

substitute for R --

1 over Z. Re-express the functional as a ratio of

polynomials in Z. The poles are the roots of the


So recapping, I started with a first-order system.

I showed you how to get a second-order system.

I showed that, in general, you can use the factor theorem to

break down the response of a higher-order system into a sum

of responses of first-order systems.

Now, I've shown that you can use the fundamental theorem of

algebra to find the poles directly, and then by knowing

the poles, you know each of the behaviors, monotonic

divergence, monotonic convergence,

or alternating signs.

And so here is this same example that I started with,

worked out by thinking about what are the poles.

The poles are 0.7 and 0.9, which we see by a simple

application of the fundamental theorem of algebra.

OK, we got a long way by just thinking about operators as


We haven't done anything that you haven't

done in high school.

Polynomials are very familiar and we've made an isomorphism

between systems and polynomials.

OK, make sure you're all with me.

Here's a higher order system.

How many of these statements are true --

0, 1, 2, 3, 4, or 5?

Talk to your neighbor, get an answer.

So how many are true --

0, 1, 2, 3, 4, or 5?

Oh, come on.

Blame it on your neighbor.

You weren't talking, but I didn't hear you not talking.

How many are true --

0, 1, 2, 3, 4, or 5?

Raise your hands.

AUDIENCE: They can't all be true.

PROFESSOR: They can't all be true.

Are they mutually contradictory?

AUDIENCE: Well, yeah.

5 --

PROFESSOR: N1 of the above, that sounds like--

OK, so you've eliminated 1.

Which one's true?

How many statements are true?

Looks like about 75%.


What should I do?

How do I figure it out?

What's my first step?

What do I do?

AUDIENCE: Operators.

PROFESSOR: Operators, absolutely.

So turn it into operators.

So take the difference equation,

turn it into operators.

The important thing to see is that there are three Y terms.

Take them all to the same side, and I get an operator

expression like that.

The ones that depend on X, there are two of them, that's

represented here.

The thing this is critical for determining poles is figuring

out the denominator.

The poles are going to come from this one.

After I get the ratio of two polynomials in R, I substitute

1 over Z for each R. So for this R, I get 1 over Z. For

this R squared, I get 1 over Z squared.

Then I want to turn it back into a ratio of polynomials in

Z, so I have to multiply top and bottom by Z squared.

And when I do that, I get this ratio of polynomials in Z.

The poles are the roots of the denominator polynomial in Z.

The poles are minus 1/2 and plus 1/4.

So the unit sample response converges to 0.

What would be the condition that that represents?

AUDIENCE: Take the polynomial on the bottom.

PROFESSOR: Something about the polynomial on the bottom.

Would all second-order systems have that property that the

unit sample response would converge to 0?



AUDIENCE: Absolute value of the poles?

PROFESSOR: Absolute value of the poles has to be?

AUDIENCE: Less than 1.

PROFESSOR: Less than 1.

If the magnitude of the poles is less than 1, then the

response magnitude will decay with time.

So that's true here, and it would be true so long as none

of the poles have a magnitude exceeding 1.

There are poles at 1/2 and 1/4.

No, that's not right.

It's -1/2 or 1/4.

There's a pole at 1/2.

No, that's not right.

There's a pole at -1/2.

There are two poles.

Yes, that's true.

None of the above.

No, that's not true.

So the answer was (2).

Everybody's comfortable?

We've done something very astonishing.

We took an arbitrary system, and we've figured out a rule

that let's us break it into the sum

of geometric sequences.

We can always write the response to a unit sample

signal, we can always write, as a weighted sum of geometric

sequences, and the number of geometric sequences in the sum

is the number of poles, which is the order of the operator

that operates on Y. OK, so we've done

something very powerful.

There's one more thing that we have to think about, and then

we have a complete picture of what's going on.

Think about when you learned polynomials.

One of the big shocks was that roots can be complex.

What would that mean?

What would it mean if we had a system whose poles were

complex valued?

So first off, does such a system exist?

Well, here's one.

I just pulled that out of the air.

If I think about the functional, 1 over (1 minus R

plus R squared) --

if I convert that into our ratio of polynomials in Z and

then find the roots, I find that the roots

have a complex part.

The roots are 1/2 plus or minus root 3 over 2j.

There's an imaginary part.

So the question is what would that mean?

Or is perhaps that system just meaningless?

Well, complex numbers work in algebra, and complex numbers

work here, too.

So the fact that a pole has a complex value in the context

of signals and systems simply means that the pole is

complex, that the base of the geometric sequence, that base

is complex.

So that means that we can still rewrite the denominator,

which was 1 minus R plus R squared, we can rewrite that

denominator in terms of a product of two first-order R


The coefficients are now complex, but it still works.

The algebra still works right.

That has to work because that's just polynomials.

That's the way polynomials behave.

Now, we can still factor it.

We can still use the factor theorem.

In fact, we can still use the fundamental theorem of algebra

to find the poles by the Z trick.

That's fine.

We can still use partial fractions.

All of these numbers are complex, but

the math still works.

The funny thing is that it implies that

the fundamental modes--

by fundamental mode, I mean the simple geometric for the

case of a first-order system, more complex behaviors for

higher order systems.

The mode is the time response associated with a pole.

So the modes are, in this case, complexes sequences.

So in general, the modes look like P0 to the n.

Here, my modes are simply have a complex value.

So what did I say they were?

The poles were 1/2 plus or minus--

so my modes simply look that.

Same thing.

The strange thing that happened was that those modes,

those geometric sequences, are now have complex values.

The first one up here, if I just look at the denominator,

these coefficients mean that it's proportionate to the mode

associated with this, which is that, which has a real part,

which is the blue part, and the imaginary part,

which is a red part.

There were two poles, plus and minus.

The other pole just flips the imaginary part.

So if I have imaginary poles, all I get is complex modes,

complex geometric sequences.

An easier way of thinking about that is thinking about--

so when we had a simple, real pole, we just had P0 to the n.

That's easy to visualize because we just think about

each time you go from 0 to 1 to 2 to 3, it goes from 1 to

P0 to P0 squared to P0 cubed.

Here, when you're multiplying complex numbers, it's easier

to imagine that on the complex plane.

Think about the location of the point 1, think about the

location of the point P0, think about the location of

the point P0 squared, and in this particular case, where

the pole was 1/2 plus or minus the square root of 3 over 2

times j, this would be pole to the 0.

This is pole to the 1.

This is pole squared, pole cubed.

As you can see when you have a complex number, the trajectory

in complex space can be complicated.

In this case, it's circular.

The circular trajectory in the complex plane corresponds to

the sinusoidal behavior in time.

So there's a correlation between the way you think

about the modes evolving on the complex plane and the way

you think about the real and imaginary

parts evolving in time.

It seems a little weird that the

response should be complex.

We're studying this kind of system theory primarily

because we're trying to gain insight into real systems.

We want to know how things like robots work.

How does the WallFinder work?

What would it mean if the WallFinder went to position

one plus the square root of 3 over 2j?

That doesn't make sense.

So there's a little bit of a strange thing going on here.

How is it that we need complex numbers to model real things?

That doesn't seem to sound right.

But the answer is that, if the difference equation had real

coefficients, as they will for a real system--

if you think about a real system, like a bank account,

the coefficients in the difference equation are real

numbers, not complex numbers.

If you think about the WallFinder system, the

coefficients in the WallFinder system, the coefficients of

the difference equations--

the coefficients of the different equations describe

the WallFinder behavior were all real numbers.

Here, I'm thinking about the denominator polynomial.

If we try to find the roots of a polynomial, and if we find a

complex root, if the coefficients were all real, it

follows that the complex conjugate of the original root

is also a root.

That's pretty simple, if you think about what it means to

be a polynomial.

If you think about a polynomial is whatever--

so I've got 1 plus Z plus Z squared plus blah, blah, blah.

2, 3 minus 16, if all of those coefficients real, then the

only way the--

If P were a root of this polynomial, then P* would have

to be a root, too, because if you complex conjugate each of

the Z's, it's the same thing as complex conjugating the

whole thing because the coefficients are real valued.

So the idea then is that if I happen to get a complex root

for my system that can be described by real value

coefficients, it must also be true that it's complex

conjugate is a root.

If that happens, the two roots co-conspire so that the modes

have canceling imaginary parts.

You can prove that.

I'm not worried about you being able to prove that.

I just want you to understand that if you have two roots

that are complex conjugates, they can conspire to have

their imaginary parts cancel, and that's

exactly what happens.

Here, in this example, the example that I started with,

where the system was 1 over (1 minus R plus R squared), the

unit sample responses showed here.

You can write as the sum of sinusoidal and cosinusoidal

signals, and the sum that falls out has the property

that the imaginary parts cancel.

It's still useful to look at the imaginary parts the same

as it is when you're trying to solve polynomials.

It's useful because the period of all of these

signals is the same.

If I think about the period of the individual modes, if I

think about the period of P0 to the n, 1/2 plus or minus

root 3 over 2j to the n, the period of that signal --

I can see in the complex plane, this is the n equals 0,

1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 --

the period is 6.

If I were to take the minus 1, I would go around the circle

the other way.

This would be zero term 1, 2, 3, 4, 5, 6, et cetera.

Both of the modes, the geometric sequence associated

with the two complex conjugate poles, both of those modes had

the same period, they're both 6, as does the

response to the real part.

So you can deduce the period of the real signal by looking

at the periods of the two complex signals.

So think about this system.

Here, I've got a system whose responses showed here.

I'm going to tell you that the response was generated by a

second-order system, that is to say a system whose

polynomial was second-order, whose polynomial in R was


Which of these statements is true?

I want to think about the pole as being a complex number.

Here, I'm showing you the complex number in polar form.

It's got a magnitude and an angle, and I'd like you to

figure how what must the magnitude have been, and what

must the angle have been.

So what's the utility?

Why did I tell you the pole in terms of it's magnitude and

angle rather than telling it to you in it's Cartesian form

as a real and imaginary part?

What's good about thinking about magnitude and angle?

Or if I break the pole into magnitude and angle, and if I

think about the mode--

the modes are always of the form P0 to the n.

If I think about modes as having a magnitude and an

angle, when I raise it to the n, something

very special happens.

What happens?

You can separate it.

What is R e to the j omega raised to the n?

That's the same as R to the n, e to the j n omega.

It's the product of a real thing times a very simple

complex thing.

What's simple about the complex thing?

The magnitude is everywhere 1 e to the j, the

magnitude of that term.

So all of the magnitude is here, none of the

magnitude is here.

All of the angle is here, none of the angle is here.

I've separated the magnitude and the angle.

So it's very insightful to think about poles in terms of

magnitude and angle because it decouples the

parts of the mode.

So I can think, then, of this complicated signal that I gave

you as being the product of a magnitude part and a pure

angle part.

From the magnitude part, I can infer something about R. R is

the ratio of the nth 1 to the n minus 1-th one, so R is a

lot bigger than 1/2.

In fact, R in this case is 0.97.

And this is pure angle.

This lets me infer something about the oscillations.

In fact, I can say something about the period.

The period is, here's a peak, here's a peak, 1, 2, 3, 4, 5,

6, 7, 8, 9, 10, 11, 12.

The period's 12.

If the period is 12, what is omega?

Omega is a number, such that by the time I got up to 12

times it, I got up to 2 pi.

What's omega?

AUDIENCE: Pi over 6.

PROFESSOR: 2 pi over 12.

So it's about 1/2.

So that's a way that I can infer the form of the answer.

I ask you what pole corresponds to this behavior.

Well, the decay that comes from the real part, that comes

from R to the n.

The oscillation, that comes from the imaginary part, and I

can figure that out by thinking about the period and

thinking about that relationship.

So the answer, then, is this one, R is between 1/2 and 1,

and omega is about 1/2.

OK, one last-- whoops, wrong button.

One last example.

So what we've seen is a very powerful way of decomposing

systems, so that we can always think about them in terms of

poles and complex geometrics, which we will call modes,

poles and modes.

And that behavior works for any system in an enormous

class of systems, so I want to think about one last one,

which is Fibonacci sequence.

You all know the Fibonacci sequence.

You've all programmed it.

We started with that when we were doing Python, and we made

some illustrations about the recursion and all that sort of

thing by thinking about it.

Now, I'm going to use signals and

systems do the same problem.

So Fibonacci was interested in population growth.

How many pairs of rabbits can be produced from a single pair

in a year if it is suppose that every month each pair

begets a new pair, from which the second month it becomes


OK, it's not quite the same English I would have used.

From this statement, you can infer a difference equation.

I've written it in terms of X. What do you think X is?

X is the input signal, and here, I'm thinking about X as

something that's specifies the initial condition.

This is the thing I alluded to earlier.

One trick that we use to make it easy to think about initial

conditions is that we embed them in the input.

So in this particular case, I'll think about the initial

condition arising from a delta function.

If I think about X as a delta, then the sequence of results

Y0, Y1, Y2, Y3 from this difference equation, is the

conventional Fibonacci sequence.

It would correspond to what if you had a baby rabbit, one

baby rabbit, that's the one, in generation

0, that's the delta.

So the input is a way that I can specify initial

conditions, and that's a very powerful way of thinking about

initial conditions.

So here's the problem.

I've got one set of baby rabbits at times 0.

They grow up.

They have baby rabbits, which grow up at the same time the

parents had more baby rabbits, at which point more babies

grow into bigger rabbits.

And big rabbits have more babies, et cetera, et cetera,

et cetera, et cetera, et cetera.

So you all know that.

You all know about Fibonacci sequence.

It blows up very quickly.

What are the poles of the Fibonacci sequence?

The difference equation looks just like the difference

equations we've looked at throughout

this hour and a half.

We can do it just like we did all the other problems.

We write the difference equation in terms of R. We

rewrite the system functional in terms of a ratio of two

polynomials in R. We substitute R goes to 1 over Z,

deduce a ratio of polynomials in Z, factor the denominator,

find the roots of the denominator, and find that

there are two poles.

The poles for the Fibonacci sequence are plus or minus the

root of 5 over 2.

That's curious.

There's no recursion there.

It's a different way of thinking about things.

There are two poles.

The first pole, the plus one --

1 plus the root of 5 over 1, corresponds to a pole whose

magnitude is bigger than 1.

It explodes.

There it is.

The second one is a negative number.

So the first one is the golden ratio, 1.618...

The second one is the negative reciprocal of the golden

ratio, which is -0.618...

And those two numbers, amazingly, conspire so that

their sum, horrendous as they are, is an integer.

And in fact, that's the integer that we computed here.

So we've used Fibonacci before to think about the way you

structure programs, iteration, recursion, that sort of thing.

Here, by thinking about signals and systems, we can

think about exactly the same problem as poles.

There's no complexity in this problem.

It doesn't take N or N squared or N log N

or anything to compute.

It's closed form.

The answer is (pole one) to the N plus (pole two) to the

N. That's it.

That's the answer.

So what we've done is we found a whole new way of thinking

about the Fibonacci sequence in terms of poles, and more

than that, we found that that way of thinking about poles

works for any difference equation of this type.

And we found that poles, this way of thinking about systems

in terms of polynomials, is a powerful abstraction that's

exactly the same kind of PCAP abstraction

that we used for Python.

The Description of Lec 5 | MIT 6.01SC Introduction to Electrical Engineering and Computer Science I, Spring 2011