{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Síntesis de redes no disipativas doblemente cargadas\n",
" \n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook aplicarán los conceptos de síntesis de inmitancias para la síntesis de un filtro pasabanda cargado tanto en su entrada como la salida. Se presentarán los parámetros scattering necesarios para la síntesis y las de dichos parámetros para redes no disipativas. \n",
"\n",
"Se usarán algunas funciones de PyTC2 para realizar los cálculos y presentar los resultados.\n",
"\n",
"* Funciones de dibujo de redes: [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_funcion_exc_abajo](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_funcion_exc_abajo), [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie)\n",
"* Funciones de síntesis de dipolos: [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [modsq2mod_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.modsq2mod_s), [trim_func_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.trim_func_s)\n",
"* Funciones para presentación de markdown y latex: [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle), [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"La síntesis de redes eléctricas no disipativas doblemente cargadas es de especial utilidad para el diseño de filtros de alta frecuencia y/o potencia. Este método fue originalmente propuesto por [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) en 1939, permitiendo que un filtro cumpla con una respuesta prescrita de pérdidas por inserción, como las de Butterworth, Chebyshev y Bessel. El gran aporte de Darlington fue relacionar las pérdidas de inserción con las de retorno, y en consecuencia con la impedancia de entrada de la red, siempre que la red sea no disipativa. Con el tiempo el método se adaptó a los **parámetros de dispersión** (S ó scattering), que están íntimamente relacionados con las pérdidas de un filtro.\n",
"\n",
"Los **parámetros S** son útiles en el análisis de redes cuando se tienen cargas en ambos puertos. Estos parámetros relacionan las ondas normalizadas incidentes ($a$) y reflejadas ($b$) en cada plano de referencia (puerto en éste contexto) del cuadripolo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se plantea el modelo lineal para el cuadripolo \n",
"\n",
"\n",
"$$ b = S \\cdot a $$\n",
"\n",
"o de forma explícita para cada puerto\n",
"\n",
"$$\n",
"\\begin{pmatrix} b_1 \\\\ b_2 \\end{pmatrix} = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} \\begin{pmatrix} a_1 \\\\ a_2 \\end{pmatrix}\n",
"$$\n",
"\n",
"Habrá dos tipos de parámetros:\n",
"\n",
"Un coeficiente de reflexión por cada puerto $x$, denominado $S_{xx}$ mientras el puerto restante está cargado con la resistencia característica $ R_{G1} = R_{G2} = R_0 $. Para un filtro de dos puertos se tiene entonces:\n",
"\n",
" $$\n",
" S_{11} = \\frac{b_1}{a_1} \\Bigg|_{a_2 = 0} = \\frac{Z_1 - R_0}{Z_1 + R_0}\n",
" $$\n",
" $$\n",
" S_{22} = \\frac{b_2}{a_2} \\Bigg|_{a_1 = 0} = \\frac{Z_2 - R_0}{Z_2 + R_0}\n",
" $$\n",
" \n",
"Siendo $Z_x$ la impedancia vista desde el plano $x$ hacia el cuadripolo cuando se conecta en el otro puerto $R_0$.\n",
"\n",
"También habrá un coeficiente de transmisión para modelar el flujo de potencia del puerto $x$ al puerto $y$, denominado $ S_{yx} $. Circuitalmente se modela con un generador $V_{gx}$ con resistencia interna $R_{Gx}$ y una carga $R_{Gy}$ en el puerto $y$. Resulta entonces\n",
"\n",
" $$\n",
" S_{21} = \\frac{b_2}{a_1} \\Bigg|_{a_2 = 0} = \\frac{V_2}{V_{G1}/2} \\cdot \\sqrt{\\frac{R_{G1}}{R_{G2}}}\n",
" $$\n",
" $$\n",
" S_{12} = \\frac{b_1}{a_2} \\Bigg|_{a_1 = 0} = \\frac{V_1}{V_{G2}/2} \\cdot \\sqrt{\\frac{R_{G2}}{R_{G1}}}\n",
" $$\n",
"\n",
"Para un sistema pasivo y no disipativo, se cumple que la matriz de parámetros $S$ es **unitaria**. Esto significa que:\n",
"\n",
"$$ S^H \\cdot S = U $$\n",
"\n",
"siendo $ S^H $ la matriz conjugada y transpuesta, o *Hermitiana*, y $U$ la matriz unitaria. Entonces los parámetros $S$ cumplen que\n",
"\n",
"$$\n",
"S_{11}^* S_{11} + S_{21}^* S_{21} =|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"$$\n",
"|S_{22}|^2 + |S_{12}|^2 = 1\n",
"$$\n",
"$$\n",
"S_{11} S_{12}^* + S_{21} S_{22}^* = 0\n",
"$$\n",
"donde el operador $*$ indica el conjugado complejo. La primera ecuación es de vital importancia, porque indica que toda la potencia disponible en el puerto 1 que no se transmita, necesariamente debe reflejarse al generador ya que el cuadripolo no podrá disiparla. La tercera ecuación también será importante más adelante cuando se generalice el enfoque. Retomando, el parámetro $|S_{11}|^2$ estará condicionado por\n",
"\n",
"$$\n",
"|S_{11}|^2 = 1 - |S_{21}|^2\n",
"$$\n",
"\n",
"que a su vez\n",
"\n",
"$$\n",
"|S_{11}|^2 = S_{11}^* S_{11} = S_{11}(-s) S_{11}(s)\n",
"$$\n",
"\n",
"es decir que podemos hallar el factor $S_{11}(s)$ a partir de una restricción en el ratio de potencias $|S_{21}|^2$. Como ya se anticipó, [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) propuso un método de síntesis muy elegante, en el que a partir de dicha restricción se condiciona la impedancia de entrada al cuadripolo, sabiendo que\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{ Z_1 - R_0}{ Z_1 + R_0}\n",
"$$\n",
"\n",
"Notar que el signo de $S_{11}$ es un grado de libertad con el que se cuenta a la hora de sintetizar. Entonces la impedancia en el puerto de entrada será, dependiendo la elección del signo de $S_{11}$\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}}\n",
"$$\n",
"\n",
"Cabe destacar que si bien $Z_1$ contempla la impedancia vista hacia un cuadripolo no disipativo, debe recordarse que la condición de medición de $S_{11}$ asume que el puerto 2 esté cargado con $R_{G2} = R_0$. Por este motivo, $Z_1$ será una impedancia RLC **muy particular**, dado que estará compuesta por una inmitancia no disipativa seguida de la carga $R_0$ al final. Esto permite que todos los métodos de síntesis vistos hasta el momento sean aplicables para diseñar el cuadripolo no disipativo, con la particularidad de que el último componente será $R_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un enfoque alternativo\n",
"\n",
"A esta altura está claro que los parámetros S de un filtro no disipativo cargado en ambos extremos está condicionado por la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"\n",
"De la misma podemos expresar, siguiendo el enfoque propuesto por *Cameron et. al.*\n",
"\n",
"$$ S_{21} = \\frac{P(s)}{\\epsilon \\cdot E(s)} $$\n",
"\n",
"donde $\\epsilon$ será un factor de escala para que ambos polinomios $P$ y $E$ sean *mónicos*. Entonces, simplificando la dependencia de $s$\n",
"\n",
"$$ |S_{21}|^2 = \\frac{P . P^*}{\\epsilon^2 \\cdot (E . E^*)} $$\n",
"\n",
"y en consecuencia\n",
"\n",
"$$\n",
"|S_{11}|^2 = \\frac{\\epsilon^2 \\cdot (E . E^*) - P . P^*}{E . E^*} = \\frac{F . F^*}{\\epsilon_R^2 \\cdot (E . E^*)} \n",
"$$\n",
"\n",
"es decir que del mismo modo que con $ S_{21}$, también se podrá expresar\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{F(s)}{\\epsilon_R \\cdot E(s)} \n",
"$$\n",
"\n",
"y es claro que todos los parámetros compartirán los mismos polos definidos por $E(s)$. Es decir que la matriz S se puede expresar como\n",
"\n",
"$$\n",
"S = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} = \\frac{1}{E(s)} \\begin{pmatrix} \\frac{F(s)}{\\epsilon_R} & \\frac{P(s)}{\\epsilon} \\\\ \\frac{P(s)}{\\epsilon} & \\frac{F(s)}{\\epsilon_R} \\end{pmatrix} \n",
"$$\n",
"\n",
"Se simplifican algunos detalles para facilitar el ejemplo que se presentará a continuación (Ver *Cogollos* cap 2. ó *Cameron et. al.* cap. 6 para un tratamiento general).\n",
"\n",
"Finalmente podemos expresar la impedancia de entrada al filtro como\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}} = R_0 \\cdot \\frac{\\epsilon_R.E \\pm F}{\\epsilon_R.E \\mp F}\n",
"$$\n",
"\n",
"Resta por aclarar cómo se calculan las constantes $\\epsilon$ y $\\epsilon_R$, que permitirán fijar las pérdidas de transmisión y retorno en frecuencias señaladas dependiendo de las características del filtro, pero eso lo dejaremos para más adelante por ahora.\n",
"\n",
"### Referencias\n",
"\n",
"1. [Richard J. Cameron, Chandra M. Kudsia, Raafat R. Mansour. *Microwave Filters for Communication Systems Fundamentals, Design and Applications. (2007). John Wiley & Sons](https://drive.google.com/file/d/1Fdr5WT8e7g9W5j4YM5Xd0-Za1BhrlQQ_/view?usp=drive_link)\n",
"2. [Cogollos Borrás, Santiago. *Fundamentos de la Teoría de Filtros*. (2016). Editorial de la Universitat Politècnica de València.](https://drive.google.com/file/d/1g4pgNS1m9dF43ZyR6LdkE-HAntfswhwh/view?usp=drive_link)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"\n",
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.remociones import remover_polo_dc, remover_polo_infinito, modsq2mod_s, trim_func_s\n",
"\n",
"from pytc2.general import print_latex, print_subtitle, a_equal_b_latex_s\n",
"from IPython.display import display, Markdown\n",
"\n",
"# Importante importar símbolos de variables \n",
"from pytc2.general import s, w, simplify_n_monic\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ejemplo 1: Pasabanda Butterworth\n",
"\n",
"Implemente un filtro pasabanda Butterworth de 4º orden con un $Q = 5$.\n",
"\n",
"Aprovechando que este método nos permite un grado de detalle tan preciso para establecer la función transferencia $S_{21}$, se procede a hallar la función transferencia que satisface dicho requerimiento. Para ello se retomamará el análisis que hicimos en el notebook [núcleo de transformación pasabanda](ejnb_transformacion_pasabanda.ipynb). En resumen, a partir de una transferencia pasabajos *Butterworth* de segundo orden, se llega a \n",
"\n",
"$$ T_{bp}(s) = \\frac{s^2 \\cdot 0.04}{s^4 + s^3 \\cdot 0.2828+s^2 \\cdot 2.04+ s \\cdot 0.2828 + 1} $$ \n",
"\n",
"luego simplemente se asume que $ S_{21} = T_{bp} $. Debido a la complejidad de la expresión, el ejercicio demandará cierta destreza algebraica que puede suplirse gracias a las capacidades simbólicas de PyTC2 y SymPy.\n",
"\n",
"Comenzamos buscando la expresión de $Z_1$ a partir de $S_{11}$, no sin antes definir $S_{21}$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{s^{2}}{25 \\left(s^{4} + \\frac{\\sqrt{2} s^{3}}{5} + \\frac{51 s^{2}}{25} + \\frac{\\sqrt{2} s}{5} + 1\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Corroboramos ganancia $\\le 1$ el centro de la banda de paso"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}(\\omega = 1)=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Resolución simbólica\n",
"\n",
"# Q de la transformación\n",
"Q_bp = sp.Rational(5)\n",
"Q_lp = sp.sqrt(2)/2\n",
"\n",
"# frecuencias de corte del pasabanda\n",
"w1, w2 = np.sort(np.abs(np.roots([1, -1/Q_bp.evalf(), -1])))\n",
"\n",
"# nucleo de transformación pasabanda\n",
"Kbp = Q_bp * (s**2 + 1) / s\n",
"\n",
"\n",
"T2proto = 1/(s**2 + s * 1/Q_lp + 1)\n",
"\n",
"H2bp = sp.simplify(sp.expand(T2proto.subs(s, Kbp)))\n",
"\n",
"S21 = H2bp\n",
"\n",
"ks21, Ps, Es = simplify_n_monic(S21)\n",
"\n",
"S21 = ks21 * Ps / Es\n",
"\n",
"print_latex(a_equal_b_latex_s('S_{21}', S21))\n",
"\n",
"print_subtitle('Corroboramos ganancia $\\le 1$ el centro de la banda de paso')\n",
"print_latex(a_equal_b_latex_s('S_{21}(\\omega = 1)', sp.simplify(sp.expand(sp.Abs(S21.subs(s, sp.I*1)))).evalf(4) ))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Luego se procede a hallar el parámetro $S_{11}$, que será su complementario en potencia."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plt_Sparams(S21sq, S11sq, bDb = True ):\n",
" \n",
" # Convertir las funciones simbólicas a funciones numéricas para evaluar\n",
" mod_S21_squared_func = sp.lambdify(w, S21sq.subs(s, sp.I*w), modules='numpy')\n",
" mod_S11_squared_func = sp.lambdify(w, S11sq.subs(s, sp.I*w), modules='numpy')\n",
" \n",
" # Crear un rango de frecuencias para graficar\n",
" frequencies = np.logspace(-1, 1, 1000)\n",
" \n",
" # Evaluar las funciones en el rango\n",
" S21_vals = mod_S21_squared_func(frequencies)\n",
" S11_vals = mod_S11_squared_func(frequencies)\n",
" \n",
" if bDb:\n",
" # Convertir a decibelios (10 * log10(valor))\n",
" S21_dB = 10 * np.log10(S21_vals)\n",
" S11_dB = 10 * np.log10(S11_vals)\n",
" s21_lbl = r'$|S_{21}|^2$ (dB)'\n",
" s11_lbl = r'$|S_{11}|^2$ (dB)'\n",
" ylbl = 'Módulo al cuadrado [dB]'\n",
" \n",
" else:\n",
" S21_dB = S21_vals\n",
" S11_dB = S11_vals\n",
" s21_lbl = r'$|S_{21}|^2$'\n",
" s11_lbl = r'$|S_{11}|^2$'\n",
" ylbl = 'Módulo al cuadrado'\n",
" \n",
" \n",
" # Graficar en escala log-log\n",
" plt.figure(1, figsize=(8, 5))\n",
" plt.semilogx(frequencies, S21_dB, label=s21_lbl, color='blue')\n",
" plt.semilogx(frequencies, S11_dB, label=s11_lbl, color='red', linestyle='--')\n",
" plt.xlabel('Frecuencia (f) [Hz]')\n",
" plt.ylabel(ylbl)\n",
" plt.title(r'Complementariedad de $|S_{21}|^2$ y $|S_{11}|^2$')\n",
" plt.legend()\n",
" plt.grid(which='both', linestyle='--', linewidth=0.5)\n",
"\n",
"\n",
"S21sq = sp.simplify(sp.expand(S21 * S21.subs(s, -s)))\n",
"\n",
"S11sq = sp.simplify(sp.expand(1 - S21sq))\n",
"\n",
"plt_Sparams(S21sq, S11sq, bDb = True)\n",
"\n",
"plt.plot( [w1, w1], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [-40, 3], 'k--' )\n",
"plt.fill([w1, w1, w2, w2], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"\n",
"plt.ylim([-40, 3])\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es interesante notar que $|S_{11}| \\leq$ 20 dB solo en una pequeña parte de la banda pasante del filtro. Es decir que podemos considerar al filtro **adaptado al generador y carga** sólo en una pequeña parte del ancho de banda. Recordar que las pérdidas por retorno ($R_L$) están estrechamente relacionadas al $|S_{11}|$ son inferiores a\n",
"\n",
"$$\n",
"R_L = -20.\\log|S_{11}|\n",
"$$\n",
"\n",
"mientras que las pérdidas por insersión $I_L$\n",
"\n",
"$$\n",
"I_L = -20.\\log|S_{21}|\n",
"$$\n",
"\n",
"También se aprecia la complementaridad de ambos parámetros $S_{21}$ y $S_{11}$, aunque es conveniente visualizarlos en unidades lineales para poder tener certeza que suman 1 a cualquier frecuencia."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt_Sparams(S21sq, S11sq, bDb = False)\n",
"\n",
"plt.plot( [w1, w1], [0, 1], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [0, 1], 'k--' )\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Asegurar que un filtro mantenga en su banda de paso las $R_L$ por debajo de 20 dB es equivalente a asegurar la adaptación al generador y a la carga, ya que el módulo de $|S_{11}| \\approx 0$ . Para ello se buscará la relación entre las $R_L$ y el parámetro $\\epsilon$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Entonces volviendo a la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1 \n",
"$$\n",
"$$\n",
"1 + \\frac{|S_{21}|^2}{|S_{11}|^2} = \\frac{1}{|S_{11}|^2} = 10^{\\frac{R_L}{10}}\n",
"$$\n",
"tenemos\n",
"$$\n",
"1 + \\frac{\\epsilon_R^2}{\\epsilon^2} \\cdot \\left|\\frac{P(s)}{F(s)}\\right|^2 = 10^{\\frac{R_L}{10}}\n",
"$$\n",
"\n",
"Como el filtro que estamos analizando no tiene ceros de transmisión en frecuencias finitas, debe cumplir la condición de unitariedad en los extremos, donde $|S_{21}|=0$, luego\n",
"\n",
"$$\n",
"\\lim_{\\omega \\to \\infty} |S_{11}(s)|^2 + 0 = 1\n",
"$$\n",
"\n",
"$$\n",
"\\lim_{\\omega \\to \\infty} \\left| \\frac{F(s)}{\\epsilon_R.E(s)} \\right| = 1\n",
"$$\n",
"\n",
"recordando que ambos polinomios son mónicos, entonces $\\epsilon_R = 1$, y en consecuencia\n",
"\n",
"$$\n",
"\\epsilon = \\frac{1}{\\sqrt{10^{R_L/10} - 1}} \\cdot \\left|\\frac{P(s)}{F(s)}\\right| \n",
"$$\n",
"\n",
"En el caso de nuestro pasabanda, partimos de un *prototipo pasabajo* donde aseguraremos $R_L \\leq$ 20 dB en la banda de paso. Para ello sabemos que $P(s)=1$ para el caso de un pasabajo *Butterworth*. Del mismo modo $F(s)= s^2$ y para $s = j.1$ r/s tendremos $\\left|F(s)\\right| = 1$. Luego simplemente resta calcular\n",
"\n",
"$$\n",
"\\epsilon = \\frac{1}{\\sqrt{10^{R_L/10} - 1}}\n",
"$$\n",
"\n",
"para $R_L = 20$ db en el límite de la banda de paso."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"RL = 20 # dB\n",
"eps_sq = sp.Rational(1)/sp.Rational(10**(RL/10)-1)\n",
"\n",
"S21sq_proto = 1/(1+ sp.Rational(eps_sq) * (s/sp.I)**4)\n",
"\n",
"S11sq_proto = sp.simplify(sp.expand(1 - S21sq_proto))\n",
"\n",
"plt_Sparams(S21sq_proto, S11sq_proto, bDb = True)\n",
"\n",
"plt.title('Prototipo pasabajo')\n",
"\n",
"plt.plot( [1., 1.], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.fill([.1, .1, 1., 1.], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"\n",
"plt.ylim([-40, 3])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se verifica entonces que el prototipo pasabajo cumple con el requerimiento de $R_L \\leq$ 20 dB para $\\omega \\leq$ 1 r/s. Luego se procede a calcular el filtro pasabanda usando este prototipo"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S21sq = sp.simplify(sp.expand(S21sq_proto.subs(s, Kbp)))\n",
"\n",
"S11sq = sp.simplify(sp.expand(1 - S21sq))\n",
"\n",
"plt_Sparams(S21sq, S11sq, bDb = True)\n",
"\n",
"plt.plot( [w1, w1], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [-40, 3], 'k--' )\n",
"plt.fill([w1, w1, w2, w2], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"plt.ylim([-40, 3])\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este caso verificamos que el pasabanda también cumple el requerimiento de $R_L$ en el ancho de banda especificado por $Q=5$.\n",
"\n",
"Ahora procedemos a calcular $S_{21}$ y $S_{11}$."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle S_{11}=\\frac{1.0 s^{4} + 2.0 s^{2} + 1.0}{1.0 s^{4} + 0.8922 s^{3} + 2.398 s^{2} + 0.8922 s + 1.0}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{0.398 s^{2}}{1.0 s^{4} + 0.8922 s^{3} + 2.398 s^{2} + 0.8922 s + 1.0}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A veces puede tener sentido forzar el signo para hallar la red\n",
"# dual.\n",
"S11_sign = 1\n",
"# s11_sign = -1\n",
"\n",
"# Cuando se demora mucho en factorizar se puede probar bTryNumeric=True\n",
"S21 = modsq2mod_s(S21sq, bTryNumeric=True)\n",
"S11 = S11_sign * modsq2mod_s(S11sq, bTryNumeric=True)\n",
"\n",
"# Truquito, S21 y S11 comparten polos\n",
"ks21, Ps, Es = simplify_n_monic(S21)\n",
"ks11, Fs, _ = simplify_n_monic(S11)\n",
"\n",
"S11 = ks11 * Fs / Es\n",
"S21 = ks21 * Ps / Es\n",
"\n",
"print_latex(a_equal_b_latex_s('S_{11}', S11.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('S_{21}', S21.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculamos entonces $Z_1$ a partir de $S_{11}$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle Z_1=\\frac{2.0 s^{4} + 0.8922 s^{3} + 4.398 s^{2} + 0.8922 s + 2.0}{s \\left(0.8922 s^{2} + 0.398 s + 0.8922\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Corroboramos $Z_1$ en el centro de la banda de paso"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_1(\\omega = 1)=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"Z1 = sp.simplify(sp.expand(Es + Fs) / sp.expand(Es - Fs))\n",
"\n",
"print_latex(a_equal_b_latex_s('Z_1', Z1.evalf(4)))\n",
"print_subtitle('Corroboramos $Z_1$ en el centro de la banda de paso')\n",
"print_latex(a_equal_b_latex_s('Z_1(\\omega = 1)', sp.simplify(sp.expand(sp.Abs(Z1.subs(s, sp.I*1)))).evalf(4) ))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora se procede con la síntesis de $Z_1$ siguiendo los ceros de la transferencia. En este caso tendremos dos ceros en cada extremo de banda."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 1º Remoción en $\\infty$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle L_1=2.242$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_2=\\frac{2.688 \\left(4.674 \\cdot 10^{-16} s^{4} + 1.169 \\cdot 10^{-16} s^{3} + 1.0 s^{2} + 0.3721 s + 0.834\\right)}{s \\left(1.0 s^{2} + 0.4461 s + 1.0\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Se descartarán los coeficientes poco significativos"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_2=\\frac{2.688 s^{2} + 1.0 s + 2.242}{1.0 s^{3} + 0.4461 s^{2} + 1.0 s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('1º Remoción en $\\infty$')\n",
"\n",
"Z2, ZL1 = remover_polo_infinito(Z1)\n",
"\n",
"# ZL1 es la impedancia removida\n",
"# extraigo L1\n",
"L1 = ZL1/s\n",
"\n",
"print_latex(a_equal_b_latex_s('L_1', L1.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Z_2', Z2.evalf(4)))\n",
"\n",
"print_subtitle('Se descartarán los coeficientes poco significativos')\n",
"\n",
"Z2 = trim_func_s(Z2)\n",
"print_latex(a_equal_b_latex_s('Z_2', Z2.evalf(4)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como puede apreciarse, las funciones de remoción fallan al aproximar el valor de los residuos y en consecuencia la remoción total nunca sucede. Para ello hace falta un poco de práctica y conocimiento del método de síntesis para proceder a la cancelación de aquellos términos residuales."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/markdown": [
"#### 2º Remoción en $0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle C_1=0.4461$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_4=\\frac{0.4461 \\left(1.0 s^{2} - 1.751 \\cdot 10^{-15} s - 2.102 \\cdot 10^{-15}\\right)}{s \\left(1.0 s^{2} + 0.4461 s + 1.0\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Poda de coeficientes y ..."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_4=\\frac{0.4461 s^{2}}{1.0 s^{3} + 0.4461 s^{2} + 1.0 s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('2º Remoción en $0$')\n",
"\n",
"Z4, ZC1 = remover_polo_dc(Z2)\n",
"\n",
"# ZC1 es la impedancia removida\n",
"# extraigo C1\n",
"C1 = 1/ZC1/s\n",
"\n",
"print_latex(a_equal_b_latex_s('C_1', C1.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Z_4', Z4.evalf(4)))\n",
"\n",
"print_subtitle('Poda de coeficientes y ...')\n",
"\n",
"Z4 = trim_func_s(Z4)\n",
"print_latex(a_equal_b_latex_s('Z_4', Z4.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"luego de haber removido en ambos extremos de impedancia, procedemos a remover en admitancia para finalizar la síntesis."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 3º Remoción en $\\infty$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle C_2=2.242$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Y_6=\\frac{1.0 s + 2.242}{s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('3º Remoción en $\\infty$')\n",
"\n",
"Y6, YC2 = remover_polo_infinito(1/Z4)\n",
"\n",
"# ZL1 es la admitancia removida\n",
"# extraigo C2\n",
"C2 = YC2/s\n",
"\n",
"Y6 = trim_func_s(Y6)\n",
"\n",
"print_latex(a_equal_b_latex_s('C_2', C2.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Y_6', Y6.evalf(4)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"en el otro extremo removeremos el último componente"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 4º Remoción en $0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle L_2=0.4461$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Y_8=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('4º Remoción en $0$')\n",
"\n",
"Y8, YL2 = remover_polo_dc(Y6)\n",
"\n",
"# YL2 es la admitancia removida\n",
"# extraigo L2\n",
"L2 = 1/YL2/s\n",
"\n",
"Y8 = trim_func_s(Y8)\n",
"\n",
"print_latex(a_equal_b_latex_s('L_2', L2.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Y_8', Y8.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y finalmente el remanente $Y_8$ es la resistencia de carga en el puerto de salida del filtro. Con esto finalizamos la síntesis y procedemos a visualizar la red resultante"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pytc2.dibujar import dibujar_puerto_entrada, dibujar_puerto_salida, dibujar_elemento_serie, dibujar_elemento_derivacion, dibujar_tanque_serie, dibujar_funcion_exc_abajo, dibujar_espacio_derivacion\n",
"from schemdraw import Drawing\n",
"\n",
"d = dibujar_puerto_entrada( Drawing(unit=4),\n",
" voltage_lbl = ('+', '$V1$', '-'), \n",
" current_lbl = '$I1$')\n",
"\n",
"d = dibujar_funcion_exc_abajo(d, 'Z_1', \n",
" Z1.evalf(4), \n",
" hacia_salida = True,\n",
" k_gap_width = 0.5)\n",
"\n",
"d = dibujar_elemento_serie(d, 'L', L1.evalf(4))\n",
"\n",
"d = dibujar_elemento_serie(d, 'C', C1.evalf(4))\n",
"\n",
"d = dibujar_elemento_derivacion(d, 'L', L2.evalf(4))\n",
"\n",
"d = dibujar_espacio_derivacion(d)\n",
"\n",
"d = dibujar_elemento_derivacion(d, 'C', C2.evalf(4))\n",
"\n",
"d = dibujar_puerto_salida(d,\n",
" voltage_lbl = ('+', '$V2$', '-'), \n",
" current_lbl = '$I2$')\n",
"\n",
"display(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finalizamos el ejemplo con una simulación circuital de los parámetros del filtro diseñado.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Con este ejemplo se finaliza el documento."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
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"cols": {
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"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
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}
![](\"./img/logo_UTN.svg\"){kind=logo}
\n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook aplicarán los conceptos de síntesis de inmitancias para la síntesis de un filtro pasabanda cargado tanto en su entrada como la salida. Se presentarán los parámetros scattering necesarios para la síntesis y las de dichos parámetros para redes no disipativas. \n",
"\n",
"Se usarán algunas funciones de PyTC2 para realizar los cálculos y presentar los resultados.\n",
"\n",
"* Funciones de dibujo de redes: [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_funcion_exc_abajo](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_funcion_exc_abajo), [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie)\n",
"* Funciones de síntesis de dipolos: [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [modsq2mod_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.modsq2mod_s), [trim_func_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.trim_func_s)\n",
"* Funciones para presentación de markdown y latex: [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle), [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"La síntesis de redes eléctricas no disipativas doblemente cargadas es de especial utilidad para el diseño de filtros de alta frecuencia y/o potencia. Este método fue originalmente propuesto por [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) en 1939, permitiendo que un filtro cumpla con una respuesta prescrita de pérdidas por inserción, como las de Butterworth, Chebyshev y Bessel. El gran aporte de Darlington fue relacionar las pérdidas de inserción con las de retorno, y en consecuencia con la impedancia de entrada de la red, siempre que la red sea no disipativa. Con el tiempo el método se adaptó a los **parámetros de dispersión** (S ó scattering), que están íntimamente relacionados con las pérdidas de un filtro.\n",
"\n",
"Los **parámetros S** son útiles en el análisis de redes cuando se tienen cargas en ambos puertos. Estos parámetros relacionan las ondas normalizadas incidentes ($a$) y reflejadas ($b$) en cada plano de referencia (puerto en éste contexto) del cuadripolo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se plantea el modelo lineal para el cuadripolo \n",
"\n",
"\n",
"$$ b = S \\cdot a $$\n",
"\n",
"o de forma explícita para cada puerto\n",
"\n",
"$$\n",
"\\begin{pmatrix} b_1 \\\\ b_2 \\end{pmatrix} = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} \\begin{pmatrix} a_1 \\\\ a_2 \\end{pmatrix}\n",
"$$\n",
"\n",
"Habrá dos tipos de parámetros:\n",
"\n",
"Un coeficiente de reflexión por cada puerto $x$, denominado $S_{xx}$ mientras el puerto restante está cargado con la resistencia característica $ R_{G1} = R_{G2} = R_0 $. Para un filtro de dos puertos se tiene entonces:\n",
"\n",
" $$\n",
" S_{11} = \\frac{b_1}{a_1} \\Bigg|_{a_2 = 0} = \\frac{Z_1 - R_0}{Z_1 + R_0}\n",
" $$\n",
" $$\n",
" S_{22} = \\frac{b_2}{a_2} \\Bigg|_{a_1 = 0} = \\frac{Z_2 - R_0}{Z_2 + R_0}\n",
" $$\n",
" \n",
"Siendo $Z_x$ la impedancia vista desde el plano $x$ hacia el cuadripolo cuando se conecta en el otro puerto $R_0$.\n",
"\n",
"También habrá un coeficiente de transmisión para modelar el flujo de potencia del puerto $x$ al puerto $y$, denominado $ S_{yx} $. Circuitalmente se modela con un generador $V_{gx}$ con resistencia interna $R_{Gx}$ y una carga $R_{Gy}$ en el puerto $y$. Resulta entonces\n",
"\n",
" $$\n",
" S_{21} = \\frac{b_2}{a_1} \\Bigg|_{a_2 = 0} = \\frac{V_2}{V_{G1}/2} \\cdot \\sqrt{\\frac{R_{G1}}{R_{G2}}}\n",
" $$\n",
" $$\n",
" S_{12} = \\frac{b_1}{a_2} \\Bigg|_{a_1 = 0} = \\frac{V_1}{V_{G2}/2} \\cdot \\sqrt{\\frac{R_{G2}}{R_{G1}}}\n",
" $$\n",
"\n",
"Para un sistema pasivo y no disipativo, se cumple que la matriz de parámetros $S$ es **unitaria**. Esto significa que:\n",
"\n",
"$$ S^H \\cdot S = U $$\n",
"\n",
"siendo $ S^H $ la matriz conjugada y transpuesta, o *Hermitiana*, y $U$ la matriz unitaria. Entonces los parámetros $S$ cumplen que\n",
"\n",
"$$\n",
"S_{11}^* S_{11} + S_{21}^* S_{21} =|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"$$\n",
"|S_{22}|^2 + |S_{12}|^2 = 1\n",
"$$\n",
"$$\n",
"S_{11} S_{12}^* + S_{21} S_{22}^* = 0\n",
"$$\n",
"donde el operador $*$ indica el conjugado complejo. La primera ecuación es de vital importancia, porque indica que toda la potencia disponible en el puerto 1 que no se transmita, necesariamente debe reflejarse al generador ya que el cuadripolo no podrá disiparla. La tercera ecuación también será importante más adelante cuando se generalice el enfoque. Retomando, el parámetro $|S_{11}|^2$ estará condicionado por\n",
"\n",
"$$\n",
"|S_{11}|^2 = 1 - |S_{21}|^2\n",
"$$\n",
"\n",
"que a su vez\n",
"\n",
"$$\n",
"|S_{11}|^2 = S_{11}^* S_{11} = S_{11}(-s) S_{11}(s)\n",
"$$\n",
"\n",
"es decir que podemos hallar el factor $S_{11}(s)$ a partir de una restricción en el ratio de potencias $|S_{21}|^2$. Como ya se anticipó, [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) propuso un método de síntesis muy elegante, en el que a partir de dicha restricción se condiciona la impedancia de entrada al cuadripolo, sabiendo que\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{ Z_1 - R_0}{ Z_1 + R_0}\n",
"$$\n",
"\n",
"Notar que el signo de $S_{11}$ es un grado de libertad con el que se cuenta a la hora de sintetizar. Entonces la impedancia en el puerto de entrada será, dependiendo la elección del signo de $S_{11}$\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}}\n",
"$$\n",
"\n",
"Cabe destacar que si bien $Z_1$ contempla la impedancia vista hacia un cuadripolo no disipativo, debe recordarse que la condición de medición de $S_{11}$ asume que el puerto 2 esté cargado con $R_{G2} = R_0$. Por este motivo, $Z_1$ será una impedancia RLC **muy particular**, dado que estará compuesta por una inmitancia no disipativa seguida de la carga $R_0$ al final. Esto permite que todos los métodos de síntesis vistos hasta el momento sean aplicables para diseñar el cuadripolo no disipativo, con la particularidad de que el último componente será $R_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un enfoque alternativo\n",
"\n",
"A esta altura está claro que los parámetros S de un filtro no disipativo cargado en ambos extremos está condicionado por la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"\n",
"De la misma podemos expresar, siguiendo el enfoque propuesto por *Cameron et. al.*\n",
"\n",
"$$ S_{21} = \\frac{P(s)}{\\epsilon \\cdot E(s)} $$\n",
"\n",
"donde $\\epsilon$ será un factor de escala para que ambos polinomios $P$ y $E$ sean *mónicos*. Entonces, simplificando la dependencia de $s$\n",
"\n",
"$$ |S_{21}|^2 = \\frac{P . P^*}{\\epsilon^2 \\cdot (E . E^*)} $$\n",
"\n",
"y en consecuencia\n",
"\n",
"$$\n",
"|S_{11}|^2 = \\frac{\\epsilon^2 \\cdot (E . E^*) - P . P^*}{E . E^*} = \\frac{F . F^*}{\\epsilon_R^2 \\cdot (E . E^*)} \n",
"$$\n",
"\n",
"es decir que del mismo modo que con $ S_{21}$, también se podrá expresar\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{F(s)}{\\epsilon_R \\cdot E(s)} \n",
"$$\n",
"\n",
"y es claro que todos los parámetros compartirán los mismos polos definidos por $E(s)$. Es decir que la matriz S se puede expresar como\n",
"\n",
"$$\n",
"S = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} = \\frac{1}{E(s)} \\begin{pmatrix} \\frac{F(s)}{\\epsilon_R} & \\frac{P(s)}{\\epsilon} \\\\ \\frac{P(s)}{\\epsilon} & \\frac{F(s)}{\\epsilon_R} \\end{pmatrix} \n",
"$$\n",
"\n",
"Se simplifican algunos detalles para facilitar el ejemplo que se presentará a continuación (Ver *Cogollos* cap 2. ó *Cameron et. al.* cap. 6 para un tratamiento general).\n",
"\n",
"Finalmente podemos expresar la impedancia de entrada al filtro como\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}} = R_0 \\cdot \\frac{\\epsilon_R.E \\pm F}{\\epsilon_R.E \\mp F}\n",
"$$\n",
"\n",
"Resta por aclarar cómo se calculan las constantes $\\epsilon$ y $\\epsilon_R$, que permitirán fijar las pérdidas de transmisión y retorno en frecuencias señaladas dependiendo de las características del filtro, pero eso lo dejaremos para más adelante por ahora.\n",
"\n",
"### Referencias\n",
"\n",
"1. [Richard J. Cameron, Chandra M. Kudsia, Raafat R. Mansour. *Microwave Filters for Communication Systems Fundamentals, Design and Applications. (2007). John Wiley & Sons](https://drive.google.com/file/d/1Fdr5WT8e7g9W5j4YM5Xd0-Za1BhrlQQ_/view?usp=drive_link)\n",
"2. [Cogollos Borrás, Santiago. *Fundamentos de la Teoría de Filtros*. (2016). Editorial de la Universitat Politècnica de València.](https://drive.google.com/file/d/1g4pgNS1m9dF43ZyR6LdkE-HAntfswhwh/view?usp=drive_link)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"\n",
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.remociones import remover_polo_dc, remover_polo_infinito, modsq2mod_s, trim_func_s\n",
"\n",
"from pytc2.general import print_latex, print_subtitle, a_equal_b_latex_s\n",
"from IPython.display import display, Markdown\n",
"\n",
"# Importante importar símbolos de variables \n",
"from pytc2.general import s, w, simplify_n_monic\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ejemplo 1: Pasabanda Butterworth\n",
"\n",
"Implemente un filtro pasabanda Butterworth de 4º orden con un $Q = 5$.\n",
"\n",
"Aprovechando que este método nos permite un grado de detalle tan preciso para establecer la función transferencia $S_{21}$, se procede a hallar la función transferencia que satisface dicho requerimiento. Para ello se retomamará el análisis que hicimos en el notebook [núcleo de transformación pasabanda](ejnb_transformacion_pasabanda.ipynb). En resumen, a partir de una transferencia pasabajos *Butterworth* de segundo orden, se llega a \n",
"\n",
"$$ T_{bp}(s) = \\frac{s^2 \\cdot 0.04}{s^4 + s^3 \\cdot 0.2828+s^2 \\cdot 2.04+ s \\cdot 0.2828 + 1} $$ \n",
"\n",
"luego simplemente se asume que $ S_{21} = T_{bp} $. Debido a la complejidad de la expresión, el ejercicio demandará cierta destreza algebraica que puede suplirse gracias a las capacidades simbólicas de PyTC2 y SymPy.\n",
"\n",
"Comenzamos buscando la expresión de $Z_1$ a partir de $S_{11}$, no sin antes definir $S_{21}$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{s^{2}}{25 \\left(s^{4} + \\frac{\\sqrt{2} s^{3}}{5} + \\frac{51 s^{2}}{25} + \\frac{\\sqrt{2} s}{5} + 1\\right)}$"
],
"text/plain": [
"