Lucas e Giovanna ATV10_CLASSE COMEÇO.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando os pacotes necessários\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Musculo:\n",
    "    \n",
    "    def __init__(self, Fmax, Lceopt, LslackPE, \n",
    "                 LslackSE, alpha, Tact, Tdeact):\n",
    "        \n",
    "        '''\n",
    "        Inputs:\n",
    "            \n",
    "            alpha: float, (rad)\n",
    "            Tact: float, (ms)\n",
    "            Tdeact: float, (ms)\n",
    "        '''\n",
    "        self.Fmax = Fmax\n",
    "        self.Lceopt = Lceopt\n",
    "        self.LslackPE = LslackPE\n",
    "        \n",
    "        self.kpe = self.Fmax/(0.04*self.LslackPE)**2\n",
    "        \n",
    "        self.LslackSE = LslackSE\n",
    "        self.kse = self.Fmax/(0.04*self.LslackSE)**2\n",
    "        \n",
    "        self.alpha = alpha\n",
    "        self.Tdeact = Tdeact/1000 #[s]\n",
    "        self.Tact = Tact/1000 #[s]\n",
    "    \n",
    "    def computaForcaParalelo(self, Lcenorm):\n",
    "    \n",
    "        if Lcenorm >= self.LslackPE/self.Lceopt:\n",
    "            Fpenorm = self.kpe*(Lcenorm - self.LslackPE/self.Lceopt)**2/self.Fmax*self.Lceopt**2\n",
    "        else:\n",
    "            Fpenorm = 0 \n",
    "        return Fpenorm \n",
    "    \n",
    "    def calculaForcaEmX(Fsenorm, Fpenorm):\n",
    "\n",
    "        Fcenorm = Fsenorm/np.cos(self.alpha)-Fpenorm\n",
    "        return Fcenorm\n",
    "    \n",
    "    def atualizaMusculo(self, Lmt, u):\n",
    "        Lsenorm = Lmtnorm - Lcenorm*np.cos(alpha)\n",
    "    \n",
    "        Fpenorm = self.computaForcaParalelo(Lcenorm)\n",
    "\n",
    "        Fsenorm = computaForcaSerie(Lsenorm)\n",
    "\n",
    "        F = normalizaForca (Fsenorm, Fmax)\n",
    "\n",
    "        Fcenorm = self.calculaForcaEmX (Fsenorm, Fpenorm)\n",
    "\n",
    "        F0 = calculoF0 (Lcenorm)  \n",
    "\n",
    "        dLcenormdt = calculoDerivadaDlcedt(VMmax,Fcenorm,a,F0,FMlen)\n",
    "\n",
    "        Lcenorm = Lcenorm + dt*dLcenormdt\n",
    "\n",
    "        a = calculaAtiv(Tact,Tdeact,u,a)\n",
    "        \n",
    "        return F, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parâmetros do sistema\n",
    "Fmax = 7400\n",
    "Lceopt = 0.093\n",
    "LslackPE = 0.093\n",
    "kpe = Fmax/(0.04*LslackPE)**2\n",
    "LslackSE = 0.223\n",
    "kse = Fmax/(0.04*LslackSE)**2\n",
    "\n",
    "alpha = (np.pi*10)/180\n",
    "Tdeact=50\n",
    "Tact=15\n",
    "\n",
    "\n",
    "dt = 0.001 # [s]\n",
    "# Vetor de tempo\n",
    "t = np.arange(0, 3, dt)\n",
    "u=1.0\n",
    "# Vetor de força\n",
    "F = np.zeros_like(t)\n",
    "\n",
    "# Vetor de comprimento\n",
    "L = np.zeros_like(t)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "musculo = Musculo(Fmax=Fmax, Lceopt=Lceopt, \n",
    "                  LslackPE=LslackPE, LslackSE=LslackSE,\n",
    "                  alpha=alpha, Tact=Tact, Tdeact=Tdeact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.093"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musculo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computaForcaSerie (Lsenorm):\n",
    "    \n",
    "    if Lsenorm >= LslackSE/Lceopt:\n",
    "        Fsenorm = kse*(Lsenorm - LslackSE/Lceopt)**2/Fmax*Lceopt**2\n",
    "    else:\n",
    "        Fsenorm = 0 \n",
    "    return Fsenorm  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizaForca (Fsenorm, Fmax):\n",
    "\n",
    "    F[i] = Fsenorm * Fmax \n",
    "    \n",
    "    return F[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculoF0 (Lcenorm):\n",
    "    \n",
    "    F0 = np.exp(-(Lcenorm-1)**2/0.45)  \n",
    "    return F0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def defineTa (Tact,Tdeact,u,a):\n",
    "\n",
    "    if u>a:\n",
    "        Ta=Tact*(0.5+1.5*a)\n",
    "    else:\n",
    "        Ta=Tdeact/(0.5+1.5*a)\n",
    "\n",
    "    return Ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def defineB (a,F0,FMlen,Fcenorm):\n",
    "\n",
    "    if Fcenorm > a*F0:\n",
    "        b = (2 + 2/0.25)*(a*F0*FMlen - Fcenorm)/(FMlen - 1)\n",
    "    else:\n",
    "        b = a*F0 + (Fcenorm/0.25)\n",
    "    \n",
    "    return b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculoDerivadaDlcedt(VMmax,Fcenorm,a,F0, FMlen):\n",
    "    b = defineB(a,F0,FMlen,Fcenorm)\n",
    "    \n",
    "    dLcenormdt = (0.25+0.75*a)*VMmax*(Fcenorm-a*F0)/b\n",
    "    return dLcenormdt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotarGraficoLMT (t,L):\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(t, L)\n",
    "    plt.xlabel('Tempo (s)')\n",
    "    plt.ylabel('Comprimento (mm)')\n",
    "    plt.title('Comprimento de L')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotarGraficoForca (t,F):\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(t, F)\n",
    "    plt.xlabel('Tempo (s)')\n",
    "    plt.ylabel('Força (N)')\n",
    "    plt.title('Gráfico da Força')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculaAtiv(Tact,Tdeact,u,a):\n",
    "    Ta = defineTa (Tact,Tdeact,u,a)\n",
    "    dadt = (u-a)/Ta \n",
    "    \n",
    "    if a<0.01:\n",
    "        a=0.01\n",
    "    a = a + dt*dadt\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Condição inicial\n",
    "Lce0 = 0.087 # [m]\n",
    "Lcenorm = Lce0/Lceopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parâmetros\n",
    "VMmax = 10\n",
    "epM0 = 0.5\n",
    "FMlen = 1.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Método de Euler\n",
    "\n",
    "a=0.01\n",
    "\n",
    "for i in range(0, len(t)):\n",
    " \n",
    "    \n",
    "    if t[i] <= 1:\n",
    "        Lmtnorm = 0.31/Lceopt\n",
    "    elif t[i] > 2:\n",
    "        Lmtnorm = 0.27/Lceopt\n",
    "    else:\n",
    "        Lmtnorm = (0.31 - (0.04*((t[i])-1)))/Lceopt\n",
    "    \n",
    "    \n",
    "    L[i] = Lmtnorm * Lceopt\n",
    "    \n",
    "    \n",
    "    Lsenorm = Lmtnorm - Lcenorm*np.cos(alpha)\n",
    "    \n",
    "    Fpenorm = musculo.computaForcaParalelo(Lcenorm)\n",
    "    \n",
    "    Fsenorm = computaForcaSerie(Lsenorm)\n",
    "\n",
    "    F[i] = normalizaForca (Fsenorm, Fmax)\n",
    "        \n",
    "    Fcenorm = musculo.calculaForcaEmX (Fsenorm, Fpenorm)\n",
    "    \n",
    "    F0 = calculoF0 (Lcenorm)  \n",
    "      \n",
    "    dLcenormdt = calculoDerivadaDlcedt(VMmax,Fcenorm,a,F0,FMlen)\n",
    "    \n",
    "    Lcenorm = Lcenorm + dt*dLcenormdt\n",
    "    \n",
    "    a = calculaAtiv(Tact,Tdeact,u,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Gráfico do Lmt\n",
    "\n",
    "plotarGraficoLMT (t,L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Gráfico da Força\n",
    "\n",
    "plotarGraficoForca (t,F)"
   ]
  },
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