# Characterization of Systems in the Time Domain

*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*

## Analysis of a Passive Electrical Network

[Electrical networks](https://en.wikipedia.org/wiki/Electrical_network) composed of linear passive elements, like resistors, capacitors and inductors can be described by linear ordinary differential equations (ODEs) with constant coefficients. Hence, in view of the theory of signals and systems they can be interpreted as linear time-invariant (LTI) systems. The different ways to characterize the properties of an LTI system introduced before are illustrated at the example of a second-order analog [low-pass filter](https://en.wikipedia.org/wiki/Low-pass_filter).



It is assumed that no energy is stored in the capacitor and inductor for $t<0$. It is furthermore assumed that $x(t) = 0$ for $t<0$. Hence $y(t) = 0$ and $\frac{d y(t)}{dt} = 0$ for $t<0$. For illustration, the normalized values $L = 0.5$, $R = 1$, $C = 0.4$ are used for the elements of the electrical network.

### Differential Equation

The differential equation describing the input/output relation of the electrical network is derived by applying [Kirchhoff's circuit laws](https://en.wikipedia.org/wiki/Kirchhoff's_circuit_laws) to the network. This results in the following ODE

\begin{equation}
C L \frac{d^2 y(t)}{dt^2} + C R \frac{d y(t)}{dt} + y(t) = x(t)
\end{equation}

This ODE is defined in `SymPy`


```python
import sympy as sym
sym.init_printing()

t, L, R, C = sym.symbols('t L R C', real=True)
x = sym.Function('x')(t)
y = sym.Function('y')(t)

ode = sym.Eq(L*C*y.diff(t, 2) + R*C*y.diff(t) + y, x)
ode
```

The normalized values of the network elements are stored in a dictionary for later substitution


```python
RLC = {R: 1, L: sym.Rational('.5'), C: sym.Rational('.4')}
RLC
```

### Impulse Response

The passive electrical network and the ODE describing its input/output relation can be interpreted as an LTI system. Hence, the system can be characterized by its [impulse response](impulse_response.ipynb) $h(t)$ which is defined as the output of the system for a Dirac Delta impulse $x(t) = \delta(t)$ at the input. For the given system, the impulse response is calculated by explicit solution of the ODE


```python
solution_h = sym.dsolve(
    ode.subs(x, sym.DiracDelta(t)).subs(y, sym.Function('h')(t)))
solution_h
```

The integration constants $C_1$ and $C_2$ have to be determined from the initial conditions $y(t) = 0$ and $\frac{d y(t)}{dt} = 0$ for $t<0$. 


```python
integration_constants = sym.solve((solution_h.rhs.limit(
    t, 0, '-'), solution_h.rhs.diff(t).limit(t, 0, '-')), ['C1', 'C2'])
integration_constants
```

Substitution of the values for the integration constants $C_1$ and $C_2$ into the result from above yields the impulse response of the low-pass


```python
h = solution_h.subs(integration_constants)
h
```

The impulse response is plotted for the values of $R$, $L$ and $C$ given above


```python
sym.plot(h.rhs.subs(RLC), (t, -1, 10), ylabel=r'h(t)');
```


    

    


### Step Response

The [step response](step_response.ipynb) is derived by integrating over the impulse response $h(t)$. For ease of illustration this is performed for the specific values of the elements given above


```python
tau = sym.symbols('tau', real=True)

he = sym.integrate(h.rhs.subs(RLC).subs(t, tau), (tau, 0, t))
he
```

Let's plot the step response


```python
sym.plot(he, (t, -1, 10), ylabel=r'$h_\epsilon(t)$');
```


    

    


### Transfer Function

For an exponential input signal $x(t) = e^{s t}$, the [transfer function](eigenfunctions.ipynb#Transfer-Function) $H(s)$ represents the complex weight of the exponential output signal $y(t) = H(s) \cdot e^{s t}$. The transfer function is derived by introducing $x(t)$ and $y(t)$ into the ODE and solving for $H(s)$


```python
s = sym.symbols('s')
H = sym.Function('H')(s)

H, = sym.solve(ode.subs(x, sym.exp(s*t)).subs(y, H*sym.exp(s*t)).doit(), H)
H
```

The transfer characteristic of an LTI system for harmonic exponential signals $e^{j \omega t} = \cos(\omega t) + j \sin(\omega t)$ is of special interest for the analysis of electrical circuits. It can be derived from $H(s)$ by substituting the complex frequency $s$ by $s = j \omega$. The resulting transfer function $H(j \omega)$ provides the attenuation/amplification and phase the system adds to an harmonic input signal.


```python
w = sym.symbols('omega', real=True)

Hjw = H.subs(s, sym.I * w)
Hjw
```

The magnitude of the transfer function $|H(j \omega)|$ is plotted for illustration for the specific values of the elements given above


```python
sym.plot(abs(Hjw.subs(RLC)), (w, -10, 10),
         ylabel=r'$|H(j \omega)|$', xlabel=r'$\omega$');
```


    

    


It now becomes obvious, that the low frequencies pass through the system and that high frequencies are attenuated. This motivates the term 'low-pass' for such systems.

As alternative to the solution of the ODE, the transfer function $H(s)$ is [computed from the impulse response](eigenfunctions.ipynb#Link-between-Transfer-Function-and-Impulse-Response) and plotted for the specific values of the elements given above


```python
H2 = sym.integrate(h.rhs.subs(RLC)*sym.exp(-s*t), (t, 0, sym.oo), conds='none')
H2.simplify()
```


```python
sym.plot(abs(H2.subs(s, sym.I*w)), (w, -10, 10),
         ylabel=r'$|H(j \omega)|$', xlabel=r'$\omega$');
```


    

    


The phase of the transfer function $\varphi(j \omega) = \arg \{ H(j \omega) \}$ provides insight into the phase added to an harmonic signal when passing through the system. It is computed and plotted for the specific values of the elements given above


```python
phi = sym.arg(Hjw)
sym.plot(phi.subs(RLC), (w, -10, 10),
         ylabel=r'$\varphi(j \omega)$', xlabel=r'$\omega$');
```


    

    


**Copyright**

This notebook is provided as [Open Educational Resource](https://en.wikipedia.org/wiki/Open_educational_resources). Feel free to use the notebook for your own purposes. The text is licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), the code of the IPython examples under the [MIT license](https://opensource.org/licenses/MIT). Please attribute the work as follows: *Sascha Spors, Continuous- and Discrete-Time Signals and Systems - Theory and Computational Examples*.

a0a5c907029609bd2a0b477124d64861a9394843
ipynb
Jupyter Notebook
systems_time_domain/network_analysis.ipynb
spatialaudio/signals-and-systems-lecture
93e2f3488dc8f7ae111a34732bd4d13116763c5d
MIT
2016-04-01T14:21:00.000Z
2022-03-28T20:35:09.000Z
systems_time_domain/network_analysis.ipynb
bagustris/signals-and-systems-lecture
08a8c7ea21f88c20b457daffe77fcca021c53137
MIT
2016-04-11T06:28:17.000Z
2021-11-10T10:59:35.000Z
systems_time_domain/network_analysis.ipynb
bagustris/signals-and-systems-lecture
08a8c7ea21f88c20b457daffe77fcca021c53137
MIT
2017-04-20T00:46:03.000Z
2022-03-30T14:07:09.000Z
Qwen/Qwen-72B
1. YES
2. YES