System Modelling/Dynamics question (time settling)

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In summary: So here is the summary: In summary, the conversation discusses the formula for settling time in a first order step function response. The percentage most often used to calculate settling time is 1% within the final value, which results in a settling time of 4.6T. However, in some cases, it may be appropriate to use a different value, such as 0.1%. The formula for calculating the time to reach 0.1% of the final value is t0.1% = t1% + ln(10)T. This is derived by taking the logarithm of the ratio of (1-the difference) and solving for t0.1%. The key is to focus on the ratio and use logarith
  • #1
KingDaniel
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Homework Statement


This is for a first order step function response, where T is the time constant and x(t) is H...(input).
For settling time, the percentage most often taken is 1% within the final value, which gives the settling time, ts, from:

0.99H = H(1 - e-ts/T)

We get, ts = 4.6T.

Now, in some applications it may be appropriate to take another value of output. For example: time to get within 0.1% of final value.
I saw this example somewhere but I don't understand their working method which was:

t0.1% = t1% + ln(10)T

I understand that the time to get within 0.1% of the final value will be "the time to get within 1% of the final value (H)" + something.
But I don't understand why it's plus ln(10)T. It's been bothering me since last night and I need to know why they did that and still got the right answer of 6.9T.

When you do it the normal way, you get 6.9T too. ie:

0.999H = H(1 - e-ts/T)
e-ts/T = 0.001
ts = -Tln(0.001)
ts = Tln(1000)
ts = 6.9T
t0.1% = 6.9T

My question is, why did they add "ln(10)T" to "the time it takes to get within 1% of the final value"?
I have a feeling I might just be overlooking something small.

Homework Equations



y(t) = H(1 - e-ts/T)

The Attempt at a Solution



At first I thought that the ln(10)T was the time it takes to cover 0.009H, because:

0.999H = 0.99H + 0.009H, which is:
t0.1% = t1% - ln(0.991)T
t0.1% = t1% + ln(1.01)T

But I guess I was wrong because clearly ln(10)T is not equal to ln(1.01)T.
 
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  • #2
Hi Dan,

You correctly guess that jumping from 0.999H = 0.99H + 0.009H to t 0.1% = t 1% - ln(0.991)T isn't right. So my question is: where did you get the idea that it is ?
If I write out the difference, I get:$$
y(t_{0.1\%}) - y(t_{1\%}) = H(1-e^{ \; t_{0.1\%}/T } ) - H(1-e^{ \; t_{1\%}/T } )
$$with, on the left 0.009 H allright, but on the right I am stuck with a difference, so pretty awkward to solve !

The advice is: you want to end up sith something that looks like $$ t_{0.1\%} - t_{1\%} = \ln(10)T\ , \rm {or} \ \ t_{0.1\%}/T - t_{1\%}/T = \ln(10)\ ,$$ so exponentiate left and right and see where that gets you.
 
  • #3
Hi @BvU , I thought it was correct because 0.99 + 0.009 = 0.999.

I think the author of the text skipped a few steps. I just realized that ln(1000) = ln(100) + ln(10), ie:

t0.1% = -Tln(0.001)
t0.1% = Tln(1000) = T[ln(100) + ln(10)]
BUT, we already know that ln(100) = t1%
so, t0.1% = t1% + ln(10)

If you have another explanation, please share it so I can get this off my chest.
 
  • #4
Yes, that's all there is to it: instead of working out the difference ##y(t_{0.1\%}) - y(t_{1\%})## , focus on the ratio of (1-the difference):$$
{1 - y(t_{0.1\%}) \over 1- y(t_{1\%}) } = {1\over 10} $$and take logarithms
 
  • #5
BvU said:
Hi Dan,

If I write out the difference, I get:$$
y(t_{0.1\%}) - y(t_{1\%}) = H(1-e^{ \; t_{0.1\%}/T } ) - H(1-e^{ \; t_{1\%}/T } )
$$with, on the left 0.009 H allright, but on the right I am stuck with a difference, so pretty awkward to solve !

@BvU , I solved this by taking ln of both sides but I got ln(0.009) instead of ln(10) as the other term:

0.009H = H(1 - e-t0.1%/T) - H(1 - e-t1%/T)
0.009 = 1 - e-t0.1%/T -1 + e-t1%/T
0.009 = - e-t0.1%/T + e-t1%/T
ln(0.009) = - ln(e-t0.1%/T) + ln(e-t1%/T)
ln(0.009) = t0.1%/T - t1%/T
Tln(0.009) = t0.1% - t1%
t0.1% = t1% + Tln(0.009)

Working seems to make sense, but still completely wrong answer...unless I can't take logs like that.
 
  • #6
Oops ! Taking the logarithm of a sum does NOT give the sum of the logarithms !

(instead, taking the logarithm of a product does give the sum of the logarithms..., and for a fraction you use ln(1/x) = -ln(x), so you get a difference )
 
  • #7
@BvU , Alright. Cheers mate
 
  • #8
You're welcome.
 

FAQ: System Modelling/Dynamics question (time settling)

1. What is system modelling and why is it important?

System modelling is the process of creating a simplified representation of a complex system in order to understand its behavior and make predictions. It is important because it allows scientists to study and analyze systems that may be too difficult or expensive to study in real life, and to make informed decisions about how to improve or control these systems.

2. What is the difference between static and dynamic system modelling?

Static system modelling focuses on the steady-state behavior of a system, while dynamic system modelling takes into account the changes over time in a system's behavior. Dynamic modelling is often more complex, but can provide a more accurate understanding of a system's behavior.

3. What is time settling in system dynamics?

Time settling refers to the amount of time it takes for a dynamic system to reach a steady state after a disturbance or change in input. It is an important measure of a system's stability and can help identify any issues or inefficiencies in the system.

4. How is system modelling used in real-world applications?

System modelling is used in a variety of fields, including engineering, economics, and social sciences. It is often used to design and optimize systems, such as transportation networks or manufacturing processes, and to make predictions about their behavior under different conditions. It can also be used to analyze and improve complex systems, such as ecosystems or economies.

5. What are some common techniques used in system modelling?

Some common techniques used in system modelling include mathematical modeling, computer simulations, and control theory. These techniques involve creating equations or algorithms to represent the behavior of a system, and using these models to make predictions and optimize the system's performance.

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