Summary: Describes Laplace transforms.
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The Laplace transform is a generalization of the Continuous-Time Fourier
Transform. However, instead of using complex sinusoids of the form
Although Laplace transforms are rarely solved using integration (tables and computers (e.g. Matlab) are much more common), we will provide the bilateral Laplace transform pair here. These define the forward and inverse Laplace transformations. Notice the similarities between the forward and inverse transforms. This will give rise to many of the same symmetries found in Fourier analysis.
Probably the most difficult and least used method for finding the Laplace transform of a signal is solving the integral. Although it is technically possible, it is extremely time consuming. Given how easy the next two methods are for finding it, we will not provide any more than this. The integrals are primarily there in order to understand where the following methods originate from.
Using a computer to find Laplace transforms is relatively
painless. Matlab has two functions,
laplace and
ilaplace, that are both part of the
symbolic toolbox, and will find the Laplace and inverse
Laplace transforms respectively. This method is generally
preferred for more complicated functions. Simpler and more
contrived functions are usually found easily enough by using tables.
When first learning about the Laplace transform, tables are the most common means for finding it. With enough practice, the tables themselves may become unnecessary, as the common transforms can become second nature. For the purpose of this section, we will focus on the inverse Laplace transform, since most design applications will begin in the Laplace domain and give rise to a result in the time domain. The method is as follows:
Compute
This can be solved directly from the table to be
Find the time domain representation,
To solve this, we first notice that
We can now extend the two previous examples by finding
To do this, we take advantage of the additive property of
linearity and the three-step method described above to
yield the result
For more complicated examples, it may be more difficult to break up the transfer function into parts that exist in a table. In this case, it is often necessary to use partial fraction expansion to get the transfer function into a more usable form.
With the Fourier transform, we had a complex-valued
function of a purely imaginary
variable,
| real and imaginary sample plots | ||||
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| magnitude and phase sample plots | ||||
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While these are legitimate ways of looking at a signal in the Laplace domain, it is quite difficult to draw and/or analyze. For this reason, a simpler method has been developed. Although it will not be discussed in detail here, the method of Poles and Zeros is much easier to understand and is the way both the Laplace transform and its discrete-time counterpart the Z-transform are represented graphically.
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