Transfer function

In engineering, a **transfer function** (also known as **system function** or **network function**) of an electronic or control system component is a mathematical function which theoretically models the device's output for each possible input. In its simplest form, this function is a two-dimensional graph of an independent scalar input versus the dependent scalar output, called a **transfer curve** or **characteristic curve**. Transfer functions for components are used to design and analyze systems assembled from components, particularly using the block diagram technique, in electronics and control theory.

The dimensions and units of the transfer function model the output response of the device for a range of possible inputs. For example, the transfer function of a two-port electronic circuit like an amplifier might be a two-dimensional graph of the scalar voltage at the output as a function of the scalar voltage applied to the input; the transfer function of an electromechanical actuator might be the mechanical displacement of the movable arm as a function of electrical current applied to the device; the transfer function of a photodetector might be the output voltage as a function of the luminous intensity of incident light of a given wavelength.

The term "transfer function" is also used in the frequency domain analysis of systems using transform methods such as the Laplace transform; here it means the amplitude of the output as a function of the frequency of the input signal. For example, the transfer function of an electronic filter is the voltage amplitude at the output as a function of the frequency of a constant amplitude sine wave applied to the input. For optical imaging devices, the optical transfer function is the Fourier transform of the point spread function (hence a function of spatial frequency).

## Linear time-invariant systems

Transfer functions are commonly used in the analysis of systems such as single-input single-output filters in the fields of signal processing, communication theory, and control theory. The term is often used exclusively to refer to linear time-invariant (LTI) systems. Most real systems have non-linear input/output characteristics, but many systems, when operated within nominal parameters (not "over-driven") have behavior close enough to linear that LTI system theory is an acceptable representation of the input/output behavior.

The descriptions below are given in terms of a complex variable,

# s
σ
j
⋅
ω

{\displaystyle s = \sigma + j \cdot \omega}

, which bears a brief explanation. In many applications, it is sufficient to define

# σ
0

{\displaystyle \sigma=0}

 (thus

# s
j
⋅
ω

{\displaystyle s = j \cdot \omega}

), which reduces the Laplace transforms with complex arguments to Fourier transforms with real argument ?. The applications where this is common are ones where there is interest only in the steady-state response of an LTI system, not the fleeting turn-on and turn-off behaviors or stability issues. That is usually the case for signal processing and communication theory.

Thus, for continuous-time input signal

x
(
t
)

{\displaystyle x(t)}

 and output

y
(
t
)

{\displaystyle y(t)}

, the transfer function

H
(
s
)

{\displaystyle H(s)}

 is the linear mapping of the Laplace transform of the input,

X
(
s
# )

L

{

x
(
t
)

}

{\displaystyle X(s) = \mathcal{L}\left\{x(t)\right\}}

, to the Laplace transform of the output

Y
(
s
# )

L

{

y
(
t
)

}

{\displaystyle Y(s) = \mathcal{L}\left\{y(t)\right\}}

:

Y
(
s
# )
H
(
s
)

X
(
s
)

{\displaystyle Y(s) = H(s)\;X(s) }

or

H
(
s
# )

Y
(
s
)

X
(
s
)

=

L

{

y
(
t
)

}

L

{

x
(
t
)

}

{\displaystyle H(s) = \frac{Y(s)}{X(s)} = \frac{ \mathcal{L}\left\{y(t)\right\} }{ \mathcal{L}\left\{x(t)\right\} } }

.
In discrete-time systems, the relation between an input signal

x
(
t
)

{\displaystyle x(t)}

 and output

y
(
t
)

{\displaystyle y(t)}

 is dealt with using the z-transform, and then the transfer function is similarly written as

H
(
z
# )

Y
(
z
)

X
(
z
)

{\displaystyle H(z) = \frac{Y(z)}{X(z)}}

 and this is often referred to as the pulse-transfer function.

### Direct derivation from differential equations

Consider a linear differential equation with constant coefficients

L
[
u
# ]

d
n

u

d

t
n

a
1

d

n
−
1

u

d

t

n
−
1

⋯

a

n
−
1

d
u

d
t

a
n

# u
r
(
t
)

{\displaystyle L[u] = \frac{d^nu}{dt^n} + a\_1\frac{d^{n-1}u}{dt^{n-1}} + \dotsb + a\_{n-1}\frac{du}{dt} + a\_nu = r(t) }

where *u* and *r* are suitably smooth functions of *t*, and *L* is the operator defined on the relevant function space, that transforms *u* into *r*. That kind of equation can be used to constrain the output function *u* in terms of the *forcing* function *r*. The transfer function can be used to define an operator

F
[
r
# ]
u

{\displaystyle F[r] = u }

 that serves as a right inverse of *L*, meaning that

L
[
F
[
r
]
# ]
r

{\displaystyle L[F[r]] = r}

.

Solutions of the *homogeneous*, constant-coefficient differential equation

L
[
u
# ]
0

{\displaystyle L[u] = 0}

 can be found by trying

# u

e

λ
t

{\displaystyle u = e^{\lambda t}}

. That substitution yields the characteristic polynomial

p
L

(
λ
# )

λ
n

a
1

λ

n
−
1

⋯

a

n
−
1

λ

a
n

{\displaystyle p\_L(\lambda) = \lambda^n + a\_1\lambda^{n-1} + \dotsb + a\_{n-1}\lambda + a\_n\,}

The inhomogeneous case can be easily solved if the input function *r* is also of the form

r
(
t
# )

e

s
t

{\displaystyle r(t) = e^{s t}}

. In that case, by substituting

# u
H
(
s
)

e

s
t

{\displaystyle u = H(s)e^{s t}}

 one finds that

L
[
H
(
s
)

e

s
t

# ]

e

s
t

{\displaystyle L[H(s) e^{s t}] = e^{s t}}

 if we define

H
(
s
# )

1

p
L

(
s
)

wherever

p
L

(
s
)
≠

{\displaystyle H(s) = \frac{1}{p\_L(s)} \qquad\text{wherever }\quad p\_L(s) \neq 0.}

Taking that as the definition of the transfer function requires careful disambiguation between complex vs. real values, which is traditionally influenced by the interpretation of *abs(H(s))* as the gain and *-atan(H(s))* as the phase lag. Other definitions of the transfer function are used: for example

1

/

p
L

(
i
k
)
.

{\displaystyle 1/p\_L(ik) .}

### Gain, transient behavior and stability

A general sinusoidal input to a system of frequency

ω
0

/

(
2
π
)

{\displaystyle \omega\_0 / (2\pi)}

 may be written

exp
⁡
(
j

ω
0

t
)

{\displaystyle \exp( j \omega\_0 t )}

. The response of a system to a sinusoidal input beginning at time

# t
0

{\displaystyle t=0}

 will consist of the sum of the steady-state response and a transient response. The steady-state response is the output of the system in the limit of infinite time, and the transient response is the difference between the response and the steady state response (It corresponds to the homogeneous solution of the above differential equation.) The transfer function for an LTI system may be written as the product:

H
(
s

# i
1

N

1

s
−

s

P
i

{\displaystyle
H(s)=\prod\_{i=1}^N \frac{1}{s-s\_{P\_i}}
}

where *sPi* are the *N* roots of the characteristic polynomial and will therefore be the poles of the transfer function. Consider the case of a transfer function with a single pole

H
(
s
# )

1

s
−

s
P

{\displaystyle H(s)=\frac{1}{s-s\_P}}

 where

s
P

=

σ
P

j

ω
P

{\displaystyle s\_P = \sigma\_P+j \omega\_P}

. The Laplace transform of a general sinusoid of unit amplitude will be

1

s
−
j

ω
i

{\displaystyle \frac{1}{s-j\omega\_i}}

. The Laplace transform of the output will be

H
(
s
)

(
s
−
j

ω
0

)

{\displaystyle \frac{H (s)}{(s-j \omega\_0)}}

 and the temporal output will be the inverse Laplace transform of that function:

g
(
t
# )

e

j

ω
0

t

−

e

(

σ
P

j

ω
P

)
t

−

σ
P

j
(

ω
0

−

ω
P

)

{\displaystyle
g(t)=\frac{e^{j\,\omega\_0\,t}-e^{(\sigma\_P+j\,\omega\_P)t}}{-\sigma\_P+j (\omega\_0-\omega\_P)}
}

The second term in the numerator is the transient response, and in the limit of infinite time it will diverge to infinity if *σP* is positive. In order for a system to be stable, its transfer function must have no poles whose real parts are positive. If the transfer function is strictly stable, the real parts of all poles will be negative, and the transient behavior will tend to zero in the limit of infinite time. The steady-state output will be:

g
(
∞
# )

e

j

ω
0

t

−

σ
P

j
(

ω
0

−

ω
P

)

{\displaystyle
g(\infty)=\frac{e^{j\, \omega\_0\,t}}{-\sigma\_P+j (\omega\_0-\omega\_P)}
}

The frequency response (or "gain") *G* of the system is defined as the absolute value of the ratio of the output amplitude to the steady-state input amplitude:

G
(

ω
i

# )

1

−

σ
P

j
(

ω
0

−

ω
P

)

=

1

σ
P
2

(

ω
P

−

ω
0

)
2

{\displaystyle
G(\omega\_i)=\left|\frac{1}{-\sigma\_P+j (\omega\_0-\omega\_P)}\right|=\frac{1}{\sqrt{\sigma\_P^2+(\omega\_P-\omega\_0)^2}}
}

which is just the absolute value of the transfer function

H
(
s
)

{\displaystyle H(s)}

 evaluated at

j

ω
i

{\displaystyle j\omega\_i }

. This result can be shown to be valid for any number of transfer function poles.

## Signal processing

Let

x
(
t
)

{\displaystyle x(t) \ }

 be the input to a general linear time-invariant system, and

y
(
t
)

{\displaystyle y(t) \ }

 be the output, and the bilateral Laplace transform of

x
(
t
)

{\displaystyle x(t) \ }

 and

y
(
t
)

{\displaystyle y(t) \ }

 be

X
(
s
)

=

L

{

x
(
t
)

}

=

d
e
f

∫

−
∞

∞

x
(
t
)

e

−
s
t

d
t
,

Y
(
s
)

=

L

{

y
(
t
)

}

=

d
e
f

∫

−
∞

∞

y
(
t
)

e

−
s
t

d
t
.

{\displaystyle \begin{align}
 X(s) &= \mathcal{L}\left \{ x(t) \right \} \ \stackrel{\mathrm{def}}{=}\ \int\_{-\infty}^{\infty} x(t) e^{-st}\, dt, \\
 Y(s) &= \mathcal{L}\left \{ y(t) \right \} \ \stackrel{\mathrm{def}}{=}\ \int\_{-\infty}^{\infty} y(t) e^{-st}\, dt.
\end{align}}

Then the output is related to the input by the transfer function

H
(
s
)

{\displaystyle H(s)}

 as

Y
(
s
# )
H
(
s
)
X
(
s
)

{\displaystyle Y(s) = H(s) X(s) \, }

and the transfer function itself is therefore

H
(
s
# )

Y
(
s
)

X
(
s
)

.

{\displaystyle H(s) = \frac{Y(s)} {X(s)}. }

In particular, if a complex harmonic signal with a sinusoidal component with amplitude

X

{\displaystyle |X| \ }

, angular frequency

ω

{\displaystyle \omega \ }

 and phase

arg
⁡
(
X
)

{\displaystyle \arg(X) \ }

, where arg is the argument

x
(
t
# )
X

e

j
ω
t

=

X

e

j
(
ω
t
arg
⁡
(
X
)
)

{\displaystyle x(t) = Xe^{j\omega t} = |X|e^{j(\omega t + \arg(X))} }

where

# X

X

e

j
arg
⁡
(
X
)

{\displaystyle X = |X|e^{j\arg(X)} }

is input to a linear time-invariant system, then the corresponding component in the output is:

y
(
t
)

=
Y

e

j
ω
t

=

Y

e

j
(
ω
t
arg
⁡
(
Y
)
)

,

Y

=

Y

e

j
arg
⁡
(
Y
)

.

{\displaystyle \begin{align}
 y(t) &= Ye^{j\omega t} = |Y|e^{j(\omega t + \arg(Y))}, \\
 Y &= |Y|e^{j\arg(Y)}.
\end{align}}

Note that, in a linear time-invariant system, the input frequency

ω

{\displaystyle \omega \ }

 has not changed, only the amplitude and the phase angle of the sinusoid has been changed by the system. The frequency response

H
(
j
ω
)

{\displaystyle H(j \omega) \ }

 describes this change for every frequency

ω

{\displaystyle \omega \ }

 in terms of *gain*:

G
(
ω
# )

Y

X

=

H
(
j
ω
)

and *phase shift*:

ϕ
(
ω
# )
arg
⁡
(
Y
)
−
arg
⁡
(
X
# )
arg
⁡
(
H
(
j
ω
)
)
.

{\displaystyle \phi(\omega) = \arg(Y) - \arg(X) = \arg( H(j \omega)).}

The phase delay (i.e., the frequency-dependent amount of delay introduced to the sinusoid by the transfer function) is:

τ

ϕ

(
ω
# )
−

ϕ
(
ω
)

ω

.

{\displaystyle \tau\_{\phi}(\omega) = -\frac{\phi(\omega)}{\omega}.}

The group delay (i.e., the frequency-dependent amount of delay introduced to the envelope of the sinusoid by the transfer function) is found by computing the derivative of the phase shift with respect to angular frequency

ω

{\displaystyle \omega \ }

,

τ

g

(
ω
# )
−

d
ϕ
(
ω
)

d
ω

.

{\displaystyle \tau\_{g}(\omega) = -\frac{d\phi(\omega)}{d\omega}.}

The transfer function can also be shown using the Fourier transform which is only a special case of the bilateral Laplace transform for the case where

# s
j
ω

{\displaystyle s=j\omega}

.

### Common transfer function families

While any LTI system can be described by some transfer function or another, there are certain "families" of special transfer functions that are commonly used.

Some common transfer function families and their particular characteristics are:

* Butterworth filter – maximally flat in passband and stopband for the given order
* Chebyshev filter (Type I) – maximally flat in stopband, sharper cutoff than a Butterworth filter of the same order
* Chebyshev filter (Type II) – maximally flat in passband, sharper cutoff than a Butterworth filter of the same order
* Bessel filter – best pulse response for a given order because it has no group delay ripple
* Elliptic filter – sharpest cutoff (narrowest transition between pass band and stop band) for the given order
* Optimum "L" filter
* Gaussian filter – minimum group delay; gives no overshoot to a step function
* Hourglass filter
* Raised-cosine filter

## Control engineering

In control engineering and control theory the transfer function is derived using the Laplace transform.

The transfer function was the primary tool used in classical control engineering. However, it has proven to be unwieldy for the analysis of multiple-input multiple-output (MIMO) systems, and has been largely supplanted by state space representations for such systems. In spite of this, a transfer matrix can always be obtained for any linear system, in order to analyze its dynamics and other properties: each element of a transfer matrix is a transfer function relating a particular input variable to an output variable.

A useful representation bridging state space and transfer function methods was proposed by Howard H. Rosenbrock and is referred to as Rosenbrock system matrix.

## Non-linear systems

Transfer functions do not properly exist for many non-linear systems. For example, they do not exist for relaxation oscillators; however, describing functions can sometimes be used to approximate such nonlinear time-invariant systems.