Low-Order Approximation of System

In summary, the conversation discusses finding a low-order approximation for a system using a given equation. The speaker has attempted to simplify the equation by dividing the numerator and denominator by 2, but is unsure about how to handle the DC gain in the (2s+5) term. They discuss dropping terms and considering the DC gain when doing low-order approximation, as well as the behavior of the system based on poles. The speaker also mentions using MATLAB to plot the equation and determine that a previously suggested solution is incorrect.
  • #1
sandy.bridge
798
1

Homework Statement


I have a pretty simple question. I was going over an older exam when I encountered something that did not quite make sense to me.

If [tex]\frac{(2s+5)(-s+0.5)}{(s+3)(s^2+0.1s+0.01)}[/tex],

find a low order approximation for the system.

I understand that the pole at s=-3 can be neglected, and that we can drop the terms containing the zeros. I also know that we need to consider the DC gain of these portions when dropping those terms for low-order approximation. What I do not understand, is how the DC gain from (2s+5) term is 5/2, rather than merely 5. Wouldn't you simply plug in a zero for s?
 
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  • #2
My approach is as follows:
Divide numerator and denominator by "2". Thus, in the numerator we have (s+2.5). Now - as a first approximation, the zero at "-2.5" and the pole at "-3" cancel each other.
This leads to a "low-order approximation" of the given function. Why do you think, that you can "neglect" the pole at "-3" ?
 
  • #3
The behaviour of the system will primarily be governed by the poles that are close the s-axis (relative to the pole at s=-3). The solution reduces the equation to 5/(12(s^2+0.1s+0.01)), but I was certain that it should be 5/(6(s^2+0.1s+0.01)).

EDIT* I plotted it in MATLAB and determined that their solution is wrong. Thanks!
 
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FAQ: Low-Order Approximation of System

What is a low-order approximation of a system?

A low-order approximation of a system is a simplified mathematical model that is used to approximate the behavior of a more complex system. It reduces the number of variables and equations needed to describe the system, making it more manageable to analyze and understand.

Why do scientists use low-order approximations?

Scientists use low-order approximations because they provide a simplified representation of a complex system, making it easier to analyze and understand its behavior. This can help in making predictions and understanding the underlying mechanisms of the system.

What are the limitations of low-order approximations?

Low-order approximations are limited in their accuracy and may not fully capture the behavior of the actual system. This is because they simplify the system by neglecting certain variables and interactions, which can affect the accuracy of the results.

How are low-order approximations calculated?

Low-order approximations are calculated using mathematical techniques such as linearization, perturbation, and truncation. These methods involve simplifying the equations of the system and reducing the number of variables to create a simplified model.

What are some applications of low-order approximations in science?

Low-order approximations have various applications in science, including physics, engineering, and economics. They are used to model and analyze complex systems such as fluid dynamics, electrical circuits, and economic systems. They can also be used to predict the behavior of systems over time and make informed decisions based on the results.

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