Finding the system output by convolution

In summary: I googled two instances of solving ODE's via convolution "with given initial conditions". Those initial conditions were all zero!
  • #1
jisbon
476
30
Homework Statement
As attached below.
Relevant Equations
-
1590373684308.png

1590373697703.png


Since there are initial conditions stated, I would have to craft the s equation in mind, in order to find the impulse by laplace inverse; which is this:
##(s^2Y(s)-sy(0)-y'(0))+8(sY(s)-y(0))+16Y(s)=x(s)##
##(s^2Y(s)+\frac{1}{2}s-1)+8(sY(s)-1)+16Y(s)=x(s)##
##Y(s)(s^2+8s+16)+\frac{1}{2}s-1-8=x(s)##
##\frac{Y(S)}{X(S)}=\frac{1}{(s^2+8s+16)+\frac{1}{2}s-9)}##

Now is this even correct(before I move on to the next step of finding the output)? It seems pretty messed up to me, but I can't figure out what is wrong here :( Please help, thanks.
 
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  • #2
You are correct so far. You might want to further simplify the denominator e.g. +16-9=7.
 
  • #3
It looks like you plugged in the wrong value for ##y(0)## in the Laplace transform of the ##y'## term.
 
  • #4
vela said:
It looks like you plugged in the wrong value for ##y(0)## in the Laplace transform of the ##y'## term.
##Y(s)(s^2+8s+16)+\frac{1}{2}s-1+4=x(s) ##
##\frac{Y(S)}{X(S)}=\frac{1}{(s^2+8s+16)+\frac{1}{2}s-3)}##
hence, inversing it will give me:
1590457422662.png

I then have to find the output, which is calculated by:
1590457447809.png

Hence the equation will be:
##\int_{-\infty}^{t} (16u(t))(\frac{2}{9}e^{-2(t-\iota)}-\frac{2}{9}e^{\frac{-13}{2}(t-\iota)})##

Does this look correct now?
 
  • #5
No, you didn't do the algebra correctly. You should get
$$Y(s) = \frac{X(s)}{s^2+8s+16} - \frac 12 \frac{s + 6}{s^2+8s+16}.$$
 
  • #6
Hi there :)
Thanks for your reply. After some figuring out, I realized that the steps that I did beforehand was unnecessary as to find the transfer function, my initial conditions has to be set to 0 in the first place.

Hence setting the initial conditions to 0,
##\frac{Y(S)}{X(S)}=\frac{1}{(s^2+8s+16)}##
Spilitting into partial fractions (in order to perform a Laplace universe to findimpulse function, I get):
##\frac{Y(S)}{X(S)}=\frac{1}{(s+4)^2}##
Inversing it will give me : ##e^{-4t}t##

So following the formula,
the output should be:
1590636418546.png

whereby it should look like:
##y(t)=\int_{\infty}^{\infty} [u(\tau)]e^{-4t}t dt##

is this correct?

ps: if it is correct, how should I proceed next? unsure of the next steps I should take.
 
  • #7
No. You need to be more careful. The limits are wrong. The integration variable is wrong. ##t-\tau## doesn't appear in your integral.

You will still have to deal somehow with the initial conditions.
 
  • #8
vela said:
No. You need to be more careful. The limits are wrong. The integration variable is wrong. ##t-\tau## doesn't appear in your integral.

You will still have to deal somehow with the initial conditions.

Sorry, I meant this: ##y(t)=\int_{-\infty}^{t} [u(\tau)]e^{-4(t-\tau)}t dt##

Also for initial conditions, what do I have to do with it? From my notes, it appears that one have to simply integrate the above function to find out the output.
 
  • #9
Hi there again.
Dug some stuff up and attempted to solve this question.

1590805350634.png

Since X(t) = 16u(t), X(s) is simply 16/s
By subbing that in the equation above, I got Y(s) =
1590805206809.png

And I inverse it to find the output Y(t) =
1590805225755.png

Is this correct?
If so, how do I determine if the system is stable? Cheers
 
  • #10
You can check your answer by plugging your solution back into the differential equation.
 
  • #11
I have never seen anyone solve a differetial equation with FINITE initial conditions by convolution. Until I'm shown to be wrong unequivocally I will state that that is impossible. Nothing like livig dangerously!

I googled two instances of solving ODE's via convolution "with given initial conditions". Those initial conditions were all zero!

PS with laplace it is of course straight-forward.
 
  • #12
rude man said:
I have never seen anyone solve a differetial equation with FINITE initial conditions by convolution. Until I'm shown to be wrong unequivocally I will state that that is impossible. Nothing like living dangerously!
Here's a trick to solve this particular problem using convolution. Let ##w(t)=−\frac 12(1+2t)e^{−4t}## and ##u(t)=y(t)−w(t)##. Then ##u(0)=u'(0)=0##, and ##u## satisfies the same differential equation ##y## does. The change of variables eliminates the non-homogeneous initial conditions. You can them solve for ##u## using convolution and add ##w## to the result to find ##y##.

You probably noticed ##w## is simply a solution to the homogeneous differential equation that satisfies the initial conditions ##y## does. In other words, this is just the usual way we solve a differential equation. Find the homogeneous and particular solutions to obtain the general solution and then set the arbitrary constants in the homogeneous solution appropriately to satisfy the initial conditions.
 
  • #13
Great post but aren't you "cheating" by coming up with the general solution in the classical (or laplace or fourier) way in the first place? You're not solving directly via convolution?

Whereas if the i.c.'s were zero the o.d.e. would simply be expressed as a laplace or Fourier transform, then inversed to get the impulse response, then convoluted. I would use graphical techniques; I have trouible with the limits of integration otherwise.

An interesting post no matter what!
 
  • #14
rude man said:
Great post but aren't you "cheating" by coming up with the general solution in the classical (or laplace or fourier) way in the first place? You're not solving directly via convolution?
Not really. You can use any function ##w## that satisfies the initial conditions. For example, if you use ##w(t) = t-\frac 12##, the differential equation ##u## satisfies is ##u'' + 8 u' + 16 u = 16 u(t) - 16 t##. The change of variables allows you to get rid of the non-homogeneous initial conditions at the cost of a more complicated convolution to find ##u(t)##.

Using the homogeneous solution with appropriate constants for ##w## is convenient because it doesn't make the differential equation any more complicated. Plus you have to solve the homogeneous differential equation to find the impulse response anyway.
 
  • #15
P.S. cute cat! I'm an ailurophile myself, currently with 1 boy.
 
  • #16
OK @vela, I just deleted a post that I hope you or anyone else never saw! o:)
Perhaps you could work a simple example for us:
## dy/dt + y = U(t) ##
## U(t)=0, t<0 ##
## U(t)=1, t>0 ##
## y(t=0+) = y_0 ##
 
  • #17
Let ##y(t) = p(t) + w(t)## where ##w(t)=y_0##. Then ##p(t) = y(t) - y_0## so that ##p(0) = 0##. The differential equation becomes
$$(p + w)' + (p+w) = p' + p + y_0 = U(t) \quad \Rightarrow \quad p' + p = U(t)-y_0.$$ The impulse response is ##h(t)=e^{-t} U(t)##, so we get
$$p(t) = \int_{-\infty}^\infty [U(\tau) - y_0][e^{-(t-\tau)} U(t-\tau)]\,d\tau = [1-e^{-t} + y_0(1- e^{-t})]U(t).$$ Therefore, we get ##y(t) = p(t) + y_0 = y_0 e^{-t} + 1-e^{-t}## for ##t>0##.

Another way you could look at the general problem is that when you have non-zero initial conditions, in the s-domain you get
$$\frac{Y(s)}{H(s)} - F(s) = X(s) \quad \Rightarrow \quad Y(s) = H(s)[X(s) + F(s)]$$ where ##F(s)## is the polynomial terms from the initial conditions. Then
$$y(t) = \int_{-\infty}^\infty [x(\tau) + f(\tau)]h(t-\tau)\,d\tau.$$

For this particular problem, we'd get ##F(s) = y_0## so ##f(t) = y_0 \delta(t)##, where ##\delta(t)## is the Dirac delta function. Then
$$y(t) = \int_{-\infty}^{\infty} [U(\tau) + y_0 \delta(\tau)][e^{-(t-\tau)}U(t-\tau)\,d\tau = (1-e^{-t} + y_0 e^{-t})U(t).$$
 
  • #18
Thanks for the trouble you took. Very illumiating.
I should have thought of using ## x(t)*h(t) \leftrightarrow X(s)H(s) ## myself. Been too long ..

Using strictly laplace I got a slightly different result for y(t):
## y(t) = 1 - e^{-t} + y_0(1-e^{-t}) ##. Big deal ...
Thx again.
 
  • #19
vela said:
Another way you could look at the general problem is that when you have non-zero initial conditions, in the s-domain you get
$$\frac{Y(s)}{H(s)} - F(s) = X(s) \quad \Rightarrow \quad Y(s) = H(s)[X(s) + F(s)]$$
I am still stuck on this part.
The correct equation surely is Y(s) = X(s)H(s) + F(s)
where ##F(s)## is the polynomial terms from the initial conditions.

For this particular problem, we'd get ##F(s) = y_0##
I think ## F(s) = y_0/s ## and no dirac delta is involved. Could this be why we got different results?
 
  • #20
I wish now I had given you
## dy/dt + ay = kU(t) ##,
## a ## in ## sec^{-1} ##. Of course, ##a## can = 1.0 sec^{-1} to preserve your previous work;

## k = 1.0 sec^{-1} ##
y dimensionless,
to maintain dimensional integrity.with associated checking facility.
 
  • #21
I got
$$sY(s) - y_0 + Y(s) = \underbrace{(s+1)}_{1/H(s)} Y(s) - y_0 = \frac 1s$$
so
$$Y(s) = H(s) \left[\frac 1s + y_0\right].$$
 
  • #22
vela said:
I got
$$sY(s) - y_0 + Y(s) = \underbrace{(s+1)}_{1/H(s)} Y(s) - y_0 = \frac 1s$$
so
$$Y(s) = H(s) \left[\frac 1s + y_0\right].$$
Right. My dumb mistake. Time to go to bed early ...
Thx.
PS is your LaTex primer available to anyone? You have a lot of \codes I've never seen before.
 

1. What is the process of finding the system output by convolution?

The process of finding the system output by convolution involves multiplying the input signal with the impulse response of the system and then integrating the resulting product over a certain range. This process is repeated for different time shifts of the input signal and the results are summed together to obtain the final output signal.

2. What is the purpose of finding the system output by convolution?

The purpose of finding the system output by convolution is to determine how a linear time-invariant system will respond to a given input signal. This is a fundamental concept in signal processing and is used to analyze and design various systems, such as filters and communication systems.

3. What are the key components needed to perform convolution?

The key components needed to perform convolution are the input signal, the impulse response of the system, and the integration process. The input signal is the signal that is being fed into the system, the impulse response is the system's response to a delta function input, and the integration process combines these two signals to obtain the system output.

4. How does convolution differ from other mathematical operations?

Convolution differs from other mathematical operations in that it is a time-domain operation, meaning it operates on signals in the time domain. Other mathematical operations, such as multiplication and addition, can be applied to signals in both the time and frequency domains. Convolution is also a non-commutative operation, meaning the order in which the signals are multiplied affects the final result.

5. What are some real-world applications of finding the system output by convolution?

There are many real-world applications of finding the system output by convolution, such as audio and image processing, communication systems, and control systems. For example, in audio processing, convolution can be used to apply different effects, such as reverb or echo, to a sound signal. In communication systems, convolution is used to analyze the effects of different channels on transmitted signals. In control systems, convolution is used to determine the response of a system to different inputs.

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