Differential Equations for EngineersXYZ Homework Edition

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6.2 Transforms of derivatives and ODEs

Transforms of derivatives

Let us see how the Laplace transform is used for differential equations. First let us try to find the Laplace transform of a function that is a derivative. Suppose g(t)g(t) is a differentiable function of exponential order, that is, |g(t)|Mect|g(t)| \leq Me^{ct} for some MM and cc. So {g(t)}\mathcal{L}\{g(t)\} exists, and what is more, limtestg(t)=0\lim_{t \rightarrow \infty}e^{-st}g(t)=0 when s>cs>c. Then

{ g ( t ) } = 0 e s t g ( t ) d t = [ e s t g ( t ) ] t = 0 0 ( s ) e s t g ( t ) d t = g ( 0 ) + s { g ( t ) } . \mathcal{L}\{g'(t)\}= \int_0^{\infty}e^{-st}g'(t)dt= \left[ e^{-st}g(t) \right]_{t=0}^{\infty}- \int_0^{\infty}(-s)e^{-st}g(t)dt=-g(0)+s \mathcal{L}\{g(t)\}. \nonumber

We repeat this procedure for higher derivatives. The results are listed in Table 1\PageIndex{1}. The procedure also works for piecewise smooth functions, that is functions that are piecewise continuous with a piecewise continuous derivative. The fact that the function is of exponential order is used to show that the limits appearing above exist. We will not worry much about this fact.

Table 1\PageIndex{1}: Laplace transforms of derivatives (G(s)={g(t)}G(s)= \mathcal{L}\{g(t)\} as usual).
f ( t ) f(t) { f ( t ) } = F ( s ) \mathcal{L}\{f(t)\}=F(s)
g ( t ) g'(t) s G ( s ) g ( 0 ) sG(s)-g(0)
g ( t ) g''(t) s 2 G ( s ) s g ( 0 ) g ( 0 ) s^2G(s)-sg(0)-g'(0)
g ( t ) g'''(t) s 3 G ( s ) s 2 g ( 0 ) s g ( 0 ) g ( 0 ) s^3G(s)-s^2g(0)-sg'(0)-g''(0)

Solving ODEs with the Laplace Transform

Notice that the Laplace transform turns differentiation into multiplication by ss. Let us see how to apply this fact to differential equations.

The procedure for linear constant coefficient equations is as follows. We take an ordinary differential equation in the time variable tt. We apply the Laplace transform to transform the equation into an algebraic (non differential) equation in the frequency domain. All the x(t)x(t), x(t)x'(t), x(t)x''(t), and so on, will be converted to X(s)X(s), sX(s)x(0)sX(s)-x(0), s2X(s)sx(0)x(0)s^2X(s) - sx(0) - x'(0), and so on. We solve the equation for X(s)X(s). Then taking the inverse transform, if possible, we find x(t)x(t).

13·s·(1+s2)113·s·(4+s2)1+(1+s2)1\frac{1}{3} \cdot s \cdot \left(1 + s^{2}\right)^{-1} - \frac{1}{3} \cdot s \cdot \left(4 + s^{2}\right)^{-1} + \left(1 + s^{2}\right)^{-1}
The inverse transform, checked in the direction that can be proved ✓ Computed · mojocas 0.1.0 ✓ Agrees with the text The inverse transform, checked in the direction that can be proved, computed exactly by mojocas 0.1.0, and confirmed to agree with the result stated in the text.That last step is the one a table performs and nothing verifies — so here it runs backwards. Take the section's own answer for x'' + x = cos(2t), the function x(t) = (1/3)cos t - (1/3)cos 2t + sin t, and transform it FORWARD: the result is exactly the X(s) the algebra produced before the table was consulted. An inverse transform is hard to certify directly; the forward transform is a finite exact table, so the claim is checked in the direction that can be proved.

It should be noted that since not every function has a Laplace transform, not every equation can be solved in this manner. Also if the equation is not a linear constant coefficient ODE, then by applying the Laplace transform we may not obtain an algebraic equation.

Using the Heaviside Function

Before we move on to more general equations than those we could solve before, we want to consider the Heaviside function. See Figure 1\PageIndex{1} for the graph.

u ( t ) = { 0 i f t < 0 , 1 i f t 0 . u(t)=\left\{ \begin{array}{cc} 0 & {\rm{if~}}t<0, \\ 1 & {\rm{if~}}t \geq 0. \end{array} \right. \nonumber

Graph that includes the negative x-axis and the horizontal right directional ray starting at (0,1)
Figure 1\PageIndex{1}: Plot of the Heaviside (unit step) function u(t)u(t).

This function is useful for putting together functions, or cutting functions off. Most commonly it is used as u(ta)u(t-a) for some constant aa. This just shifts the graph to the right by aa. That is, it is a function that is 0 when <a<a and 1 when tat \ge a. Suppose for example that f(t)f(t) is a “signal” and you started receiving the signal sint\sin t at time t=πt = \pi. The function f(t)f(t) should then be defined as

f ( t ) = { 0 i f t < π , sin t i f t π . f(t)=\left\{ \begin{array}{cc} 0 & {\rm{if~}}t< \pi, \\ \sin t & {\rm{if~}}t \geq \pi. \end{array} \right. \nonumber

Using the Heaviside function, f(t)f(t) can be written as

f ( t ) = u ( t π ) sin t f(t) = u(t- \pi) \sin t \nonumber

Similarly the step function that is 11 on the interval [1,2)[1,2) and zero everywhere else can be written as

u ( t 1 ) u ( t 2 ) . u(t-1) - u(t-2). \nonumber

The Heaviside function is useful to define functions defined piecewise. If you want to define f(t)f(t) such that f(t)=tf(t)=t when tt is in [0,1][0,1], f(t)=t+2f(t) = -t +2 when tt is in [1,2)[1,2) and f(t)=0f(t)=0 otherwise, you can use the expression

f ( t ) = t ( u ( t ) u ( t 1 ) ) + ( t + 2 ) ( u ( t 1 ) u ( t 2 ) ) . f(t) = t \left(u(t) -u(t-1)\right) + (-t+2) \left(u(t-1)-u(t-2) \right). \nonumber

Hence it is useful to know how the Heaviside function interacts with the Laplace transform. We have already seen that

{ u ( t a ) } = e a s 2 . \mathcal{L} \{ u(t-a)\} = \dfrac{e^{-as}}{2}. \nonumber

Shifting Property

This can be generalized into a shifting property or second shifting property.

{ f ( t a ) u ( t a ) } = e a s { f ( t ) } . \label{eq:14} \mathcal{L} \{ f(t-a)u(t-a) \} = e^{-as} \mathcal{L} \{ f(t) \}.

(6.2.1)

Transfer Functions

Laplace transform leads to the following useful concept for studying the steady state behavior of a linear system. Suppose we have an equation of the form

L x = f ( t ) , Lx = f(t), \nonumber

where LL is a linear constant coefficient differential operator. Then f(t)f(t) is usually thought of as input of the system and x(t)x(t) is thought of as the output of the system. For example, for a mass-spring system the input is the forcing function and output is the behavior of the mass. We would like to have an convenient way to study the behavior of the system for different inputs.

Let us suppose that all the initial conditions are zero and take the Laplace transform of the equation, we obtain the equation

A ( s ) X ( s ) = F ( s ) . A(s)X(s) = F(s). \nonumber

Solving for the ratio X(s)F(s)\frac{X(s)}{F(s)} we obtain the so-called transfer function H(s)=1A(s)H(s)=\frac{1}{A(s)}.

H ( s ) = X ( s ) F ( s ) H(s) = \dfrac{X(s)}{F(s)} \nonumber

In other words, X(s)=H(s)F(s)X(s) = H(s)F(s). We obtain an algebraic dependence of the output of the system based on the input. We can now easily study the steady state behavior of the system given different inputs by simply multiplying by the transfer function.

Transforms of Integrals

A feature of Laplace transforms is that it is also able to easily deal with integral equations. That is, equations in which integrals rather than derivatives of functions appear. The basic property, which can be proved by applying the definition and doing integration by parts, is

{ 0 t f ( τ ) d τ } = 1 s F ( s ) . \mathcal{L} \left\{ \int_0^t f(\tau) \, d\tau \right\} = \dfrac{1}{s}F(s). \nonumber

It is sometimes useful (e.g. for computing the inverse transform) to write this as

0 t f ( τ ) d τ = 1 { 1 s F ( s ) } . \int_0^t f(\tau) \, d\tau = \mathcal{L}^{-1} \left\{\dfrac{1}{s}F(s)\right\}. \nonumber

Adapted from Differential Equations for Engineers by Jiří Lebl (https://www.jirka.org/diffyqs/), © Jiří Lebl, licensed under CC BY-SA 4.0. Changes were made. License: CC-BY-SA-4.0.

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