{
 "cells": [
  {
   "attachments": {
    "pdc_overview.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TCLab Overview\n",
    "\n",
    "![pdc_overview.png](attachment:pdc_overview.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Step Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tclab\n",
    "import time\n",
    "import os.path\n",
    "\n",
    "# generate step test data on Arduino\n",
    "filename = 'data.csv'\n",
    "\n",
    "# redo data collection?\n",
    "redo = False\n",
    "\n",
    "# check if file already exists\n",
    "if os.path.isfile(filename) and (not redo):\n",
    "    print('File: '+filename+' already exists.')\n",
    "    print('Change redo=True to collect data again')\n",
    "    print('TCLab should be at room temperature at start')\n",
    "else:\n",
    "    # heater steps\n",
    "    Q1d = np.zeros(601)\n",
    "    Q1d[10:200] = 80\n",
    "    Q1d[200:280] = 20\n",
    "    Q1d[280:400] = 70\n",
    "    Q1d[400:] = 50\n",
    "\n",
    "    Q2d = np.zeros(601)\n",
    "    Q2d[120:320] = 100\n",
    "    Q2d[320:520] = 10\n",
    "    Q2d[520:] = 80\n",
    "\n",
    "    # Connect to Arduino\n",
    "    a = tclab.TCLab()\n",
    "    fid = open(filename,'w')\n",
    "    fid.write('Time,Q1,Q2,T1,T2\\n')\n",
    "    fid.close()\n",
    "\n",
    "    # run step test (10 min)\n",
    "    for i in range(601):\n",
    "        # set heater values\n",
    "        a.Q1(Q1d[i])\n",
    "        a.Q2(Q2d[i])\n",
    "        print('Time: ' + str(i) + \\\n",
    "              ' Q1: ' + str(Q1d[i]) + \\\n",
    "              ' Q2: ' + str(Q2d[i]) + \\\n",
    "              ' T1: ' + str(a.T1)   + \\\n",
    "              ' T2: ' + str(a.T2))\n",
    "        # wait 1 second\n",
    "        time.sleep(1)\n",
    "        fid = open(filename,'a')\n",
    "        fid.write(str(i)+','+str(Q1d[i])+','+str(Q2d[i])+',' \\\n",
    "                  +str(a.T1)+','+str(a.T2)+'\\n')\n",
    "    # close connection to Arduino\n",
    "    a.close()\n",
    "    fid.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Step Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# read data file\n",
    "filename = 'data.csv'\n",
    "data = pd.read_csv(filename)\n",
    "\n",
    "# plot measurements\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(data['Time'],data['Q1'],'r-',label='Heater 1')\n",
    "plt.plot(data['Time'],data['Q2'],'b--',label='Heater 2')\n",
    "plt.ylabel('Heater (%)')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(data['Time'],data['T1'],'r.',label='Temperature 1')\n",
    "plt.plot(data['Time'],data['T2'],'b.',label='Temperature 2')\n",
    "plt.ylabel(r'Temperature ($^oC$)')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.savefig('data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Physics-based Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gekko"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from gekko import GEKKO\n",
    "from scipy.integrate import odeint\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "# Import data\n",
    "try:\n",
    "    # try to read local data file first\n",
    "    filename = 'data.csv'\n",
    "    data = pd.read_csv(filename)\n",
    "except:\n",
    "    filename = 'http://apmonitor.com/pdc/uploads/Main/tclab_data2.txt'\n",
    "    data = pd.read_csv(filename)\n",
    "\n",
    "# Fit Parameters of Energy Balance\n",
    "m = GEKKO() # Create GEKKO Model\n",
    "\n",
    "# Parameters to Estimate\n",
    "U = m.FV(value=10,lb=1,ub=20)\n",
    "Us = m.FV(value=20,lb=5,ub=40)\n",
    "alpha1 = m.FV(value=0.01,lb=0.001,ub=0.03)  # W / % heater\n",
    "alpha2 = m.FV(value=0.005,lb=0.001,ub=0.02) # W / % heater\n",
    "tau = m.FV(value=10.0,lb=5.0,ub=60.0)\n",
    "\n",
    "# Measured inputs\n",
    "Q1 = m.Param()\n",
    "Q2 = m.Param()\n",
    "\n",
    "Ta =23.0+273.15                     # K\n",
    "mass = 4.0/1000.0                   # kg\n",
    "Cp = 0.5*1000.0                     # J/kg-K    \n",
    "A = 10.0/100.0**2                   # Area not between heaters in m^2\n",
    "As = 2.0/100.0**2                   # Area between heaters in m^2\n",
    "eps = 0.9                           # Emissivity\n",
    "sigma = 5.67e-8                     # Stefan-Boltzmann\n",
    "\n",
    "TH1 = m.SV()\n",
    "TH2 = m.SV()\n",
    "TC1 = m.CV()\n",
    "TC2 = m.CV()\n",
    "\n",
    "# Heater Temperatures in Kelvin\n",
    "T1 = m.Intermediate(TH1+273.15)\n",
    "T2 = m.Intermediate(TH2+273.15)\n",
    "\n",
    "# Heat transfer between two heaters\n",
    "Q_C12 = m.Intermediate(Us*As*(T2-T1)) # Convective\n",
    "Q_R12 = m.Intermediate(eps*sigma*As*(T2**4-T1**4)) # Radiative\n",
    "\n",
    "# Energy balances\n",
    "m.Equation(TH1.dt() == (1.0/(mass*Cp))*(U*A*(Ta-T1) \\\n",
    "                + eps * sigma * A * (Ta**4 - T1**4) \\\n",
    "                + Q_C12 + Q_R12 \\\n",
    "                + alpha1*Q1))\n",
    "\n",
    "m.Equation(TH2.dt() == (1.0/(mass*Cp))*(U*A*(Ta-T2) \\\n",
    "                + eps * sigma * A * (Ta**4 - T2**4) \\\n",
    "                - Q_C12 - Q_R12 \\\n",
    "                + alpha2*Q2))\n",
    "\n",
    "# Conduction to temperature sensors\n",
    "m.Equation(tau*TC1.dt() == TH1-TC1)\n",
    "m.Equation(tau*TC2.dt() == TH2-TC2)\n",
    "\n",
    "# Options\n",
    "# STATUS=1 allows solver to adjust parameter\n",
    "U.STATUS = 1  \n",
    "Us.STATUS = 1  \n",
    "alpha1.STATUS = 1 \n",
    "alpha2.STATUS = 1\n",
    "tau.STATUS = 1\n",
    "\n",
    "Q1.value=data['Q1'].values\n",
    "Q2.value=data['Q2'].values\n",
    "TH1.value=data['T1'].values[0]\n",
    "TH2.value=data['T2'].values[0]\n",
    "TC1.value=data['T1'].values\n",
    "TC1.FSTATUS = 1    # minimize fstatus * (meas-pred)^2\n",
    "TC2.value=data['T2'].values\n",
    "TC2.FSTATUS = 1    # minimize fstatus * (meas-pred)^2\n",
    "\n",
    "m.time = data['Time'].values\n",
    "m.options.IMODE   = 5 # MHE\n",
    "m.options.EV_TYPE = 2 # Objective type\n",
    "m.options.NODES   = 2 # Collocation nodes\n",
    "m.options.SOLVER  = 3 # IPOPT\n",
    "\n",
    "m.solve(disp=False) # Solve\n",
    "\n",
    "# Parameter values\n",
    "print('U     : ' + str(U.value[0]))\n",
    "print('Us     : ' + str(Us.value[0]))\n",
    "print('alpha1: ' + str(alpha1.value[0]))\n",
    "print('alpha2: ' + str(alpha2.value[-1]))\n",
    "print('tau: ' + str(tau.value[0]))\n",
    "\n",
    "sae = 0.0\n",
    "for i in range(len(data)):\n",
    "    sae += np.abs(data['T1'][i]-TC1.value[i])\n",
    "    sae += np.abs(data['T2'][i]-TC2.value[i])\n",
    "print('SAE Energy Balance: ' + str(sae))\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(10,7))\n",
    "ax=plt.subplot(2,1,1)\n",
    "ax.grid()\n",
    "plt.plot(data['Time'],data['T1'],'r.',label=r'$T_1$ measured')\n",
    "plt.plot(m.time,TC1.value,color='black',linestyle='--',\\\n",
    "         linewidth=2,label=r'$T_1$ energy balance')\n",
    "plt.plot(data['Time'],data['T2'],'b.',label=r'$T_2$ measured')\n",
    "plt.plot(m.time,TC2.value,color='orange',linestyle='--',\\\n",
    "         linewidth=2,label=r'$T_2$ energy balance')\n",
    "plt.ylabel(r'T ($^oC$)')\n",
    "plt.legend(loc=2)\n",
    "ax=plt.subplot(2,1,2)\n",
    "ax.grid()\n",
    "plt.plot(data['Time'],data['Q1'],'r-',\\\n",
    "         linewidth=3,label=r'$Q_1$')\n",
    "plt.plot(data['Time'],data['Q2'],'b:',\\\n",
    "         linewidth=3,label=r'$Q_2$')\n",
    "plt.ylabel('Heaters')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('Physics_fit.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine FOPDT Parameters with Graphical Fit Widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "import ipywidgets as wg\n",
    "from IPython.display import display\n",
    "\n",
    "# try to read local data file first\n",
    "try:\n",
    "    filename = 'data.csv'\n",
    "    data = pd.read_csv(filename)\n",
    "except:\n",
    "    filename = 'http://apmonitor.com/pdc/uploads/Main/tclab_data2.txt'\n",
    "    data = pd.read_csv(filename)\n",
    "    \n",
    "n = 601 # time points to plot\n",
    "tf = 600.0 # final time\n",
    "\n",
    "# Use expected room temperature for initial condition\n",
    "#y0 = [23.0,23.0]\n",
    "# Use initial condition\n",
    "y0d = [data['T1'].values[0],data['T2'].values[0]]\n",
    "\n",
    "# load data\n",
    "Q1 = data['Q1'].values\n",
    "Q2 = data['Q2'].values\n",
    "T1 = data['T1'].values\n",
    "T2 = data['T2'].values\n",
    "T1p = np.ones(n)*y0d[0]\n",
    "T2p = np.ones(n)*y0d[1]\n",
    "\n",
    "def process(y,t,u1,u2,Kp,Kd,taup):\n",
    "    y1,y2 = y\n",
    "    dy1dt = (1.0/taup) * (-(y1-y0d[0]) + Kp * u1 + Kd * (y2-y1))\n",
    "    dy2dt = (1.0/taup) * (-(y2-y0d[1]) + (Kp/2.0) * u2 + Kd * (y1-y2))\n",
    "    return [dy1dt,dy2dt]\n",
    "\n",
    "def fopdtPlot(Kp,Kd,taup,thetap):\n",
    "    y0 = y0d\n",
    "    t = np.linspace(0,tf,n) # create time vector\n",
    "    iae = 0.0\n",
    "    # loop through all time steps\n",
    "    for i in range(1,n):\n",
    "        # simulate process for one time step\n",
    "        ts = [t[i-1],t[i]]         # time interval\n",
    "        inputs = (Q1[max(0,i-int(thetap))],Q2[max(0,i-int(thetap))],Kp,Kd,taup)\n",
    "        y = odeint(process,y0,ts,args=inputs)\n",
    "        y0 = y[1]                  # record new initial condition\n",
    "        T1p[i] = y0[0]\n",
    "        T2p[i] = y0[1]        \n",
    "        iae += np.abs(T1[i]-T1p[i]) + np.abs(T2[i]-T2p[i])\n",
    "\n",
    "    # plot FOPDT response\n",
    "    plt.figure(1,figsize=(15,7))\n",
    "    plt.subplot(2,1,1)\n",
    "    plt.plot(t,T1,'r.',linewidth=2,label='Temperature 1 (meas)')\n",
    "    plt.plot(t,T2,'b.',linewidth=2,label='Temperature 2 (meas)')\n",
    "    plt.plot(t,T1p,'r--',linewidth=2,label='Temperature 1 (pred)')\n",
    "    plt.plot(t,T2p,'b--',linewidth=2,label='Temperature 2 (pred)')\n",
    "    plt.ylabel(r'T $(^oC)$')\n",
    "    plt.text(200,20,'Integral Abs Error: ' + str(np.round(iae,2)))\n",
    "    plt.text(400,35,r'$K_p$: ' + str(np.round(Kp,0)))  \n",
    "    plt.text(400,30,r'$K_d$: ' + str(np.round(Kd,0)))  \n",
    "    plt.text(400,25,r'$\\tau_p$: ' + str(np.round(taup,0)) + ' sec')  \n",
    "    plt.text(400,20,r'$\\theta_p$: ' + str(np.round(thetap,0)) + ' sec')  \n",
    "    plt.legend(loc=2)\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.plot(t,Q1,'b--',linewidth=2,label=r'Heater 1 ($Q_1$)')\n",
    "    plt.plot(t,Q2,'r:',linewidth=2,label=r'Heater 2 ($Q_2$)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('time (sec)')\n",
    "\n",
    "Kp_slide = wg.FloatSlider(value=0.5,min=0.1,max=1.5,step=0.05)\n",
    "Kd_slide = wg.FloatSlider(value=0.0,min=0.0,max=1.0,step=0.05)\n",
    "taup_slide = wg.FloatSlider(value=50.0,min=50.0,max=250.0,step=5.0)\n",
    "thetap_slide = wg.FloatSlider(value=0,min=0,max=30,step=1)\n",
    "wg.interact(fopdtPlot, Kp=Kp_slide, Kd=Kd_slide, taup=taup_slide,thetap=thetap_slide)\n",
    "print('FOPDT Simulator: Adjust Kp, Kd, taup, and thetap ' + \\\n",
    "      'to achieve lowest Integral Abs Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine FOPDT Parameters with Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "from scipy.optimize import minimize\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "# initial guesses\n",
    "x0 = np.zeros(4)\n",
    "x0[0] = 0.8 # Kp\n",
    "x0[1] = 0.2 # Kd\n",
    "x0[2] = 150.0 # taup\n",
    "x0[3] = 10.0 # thetap\n",
    "\n",
    "# Import CSV data file\n",
    "# try to read local data file first\n",
    "try:\n",
    "    filename = 'data.csv'\n",
    "    data = pd.read_csv(filename)\n",
    "except:\n",
    "    filename = 'http://apmonitor.com/pdc/uploads/Main/tclab_data2.txt'\n",
    "    data = pd.read_csv(filename)\n",
    "Q1_0 = data['Q1'].values[0]\n",
    "Q2_0 = data['Q2'].values[0]\n",
    "T1_0 = data['T1'].values[0]\n",
    "T2_0 = data['T2'].values[0]\n",
    "t = data['Time'].values - data['Time'].values[0]\n",
    "Q1 = data['Q1'].values\n",
    "Q2 = data['Q2'].values\n",
    "T1 = data['T1'].values\n",
    "T2 = data['T2'].values\n",
    "\n",
    "# specify number of steps\n",
    "ns = len(t)\n",
    "delta_t = t[1]-t[0]\n",
    "# create linear interpolation of the u data versus time\n",
    "Qf1 = interp1d(t,Q1)\n",
    "Qf2 = interp1d(t,Q2)\n",
    "\n",
    "# define first-order plus dead-time approximation    \n",
    "def fopdt(T,t,Qf1,Qf2,Kp,Kd,taup,thetap):\n",
    "    #  T      = states\n",
    "    #  t      = time\n",
    "    #  Qf1    = input linear function (for time shift)\n",
    "    #  Qf2    = input linear function (for time shift)\n",
    "    #  Kp     = model gain\n",
    "    #  Kd     = disturbance gain\n",
    "    #  taup   = model time constant\n",
    "    #  thetap = model time constant\n",
    "    # time-shift Q\n",
    "    try:\n",
    "        if (t-thetap) <= 0:\n",
    "            Qm1 = Qf1(0.0)\n",
    "            Qm2 = Qf2(0.0)\n",
    "        else:\n",
    "            Qm1 = Qf1(t-thetap)\n",
    "            Qm2 = Qf2(t-thetap)\n",
    "    except:\n",
    "        Qm1 = Q1_0\n",
    "        Qm2 = Q2_0\n",
    "    # calculate derivative\n",
    "    dT1dt = (-(T[0]-T1_0) + Kp*(Qm1-Q1_0) + Kd*(T[1]-T[0]))/taup\n",
    "    dT2dt = (-(T[1]-T2_0) + (Kp/2.0)*(Qm2-Q2_0) + Kd*(T[0]-T[1]))/taup\n",
    "    return [dT1dt,dT2dt]\n",
    "\n",
    "# simulate FOPDT model\n",
    "def sim_model(x):\n",
    "    # input arguments\n",
    "    Kp,Kd,taup,thetap = x\n",
    "    # storage for model values\n",
    "    T1p = np.ones(ns) * T1_0\n",
    "    T2p = np.ones(ns) * T2_0\n",
    "    # loop through time steps    \n",
    "    for i in range(0,ns-1):\n",
    "        ts = [t[i],t[i+1]]\n",
    "        T = odeint(fopdt,[T1p[i],T2p[i]],ts,args=(Qf1,Qf2,Kp,Kd,taup,thetap))\n",
    "        T1p[i+1] = T[-1,0]\n",
    "        T2p[i+1] = T[-1,1]\n",
    "    return T1p,T2p\n",
    "\n",
    "# define objective\n",
    "def objective(x):\n",
    "    # simulate model\n",
    "    T1p,T2p = sim_model(x)\n",
    "    # return objective\n",
    "    return sum(np.abs(T1p-T1)+np.abs(T2p-T2))\n",
    "\n",
    "# show initial objective\n",
    "print('Initial SSE Objective: ' + str(objective(x0)))\n",
    "print('Optimizing Values...')\n",
    "\n",
    "# optimize without parameter constraints\n",
    "#solution = minimize(objective,x0)\n",
    "\n",
    "# optimize with bounds on variables\n",
    "bnds = ((0.4, 1.5), (0.1, 0.5), (50.0, 200.0), (0.0, 30.0))\n",
    "solution = minimize(objective,x0,bounds=bnds,method='SLSQP')\n",
    "\n",
    "# show final objective\n",
    "x = solution.x\n",
    "iae = objective(x)\n",
    "Kp,Kd,taup,thetap = x\n",
    "print('Final SSE Objective: ' + str(objective(x)))\n",
    "print('Kp: ' + str(Kp))\n",
    "print('Kd: ' + str(Kd))\n",
    "print('taup: ' + str(taup))\n",
    "print('thetap: ' + str(thetap))\n",
    "# save fopdt.txt file\n",
    "fid = open('fopdt.txt','w')\n",
    "fid.write(str(Kp)+'\\n')\n",
    "fid.write(str(Kd)+'\\n')\n",
    "fid.write(str(taup)+'\\n')\n",
    "fid.write(str(thetap)+'\\n')\n",
    "fid.write(str(T1_0)+'\\n')\n",
    "fid.write(str(T2_0)+'\\n')\n",
    "fid.close()\n",
    "\n",
    "# calculate model with updated parameters\n",
    "T1p,T2p = sim_model(x)\n",
    "\n",
    "plt.figure(1,figsize=(15,7))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(t,T1,'r.',linewidth=2,label='Temperature 1 (meas)')\n",
    "plt.plot(t,T2,'b.',linewidth=2,label='Temperature 2 (meas)')\n",
    "plt.plot(t,T1p,'r--',linewidth=2,label='Temperature 1 (pred)')\n",
    "plt.plot(t,T2p,'b--',linewidth=2,label='Temperature 2 (pred)')\n",
    "plt.ylabel(r'T $(^oC)$')\n",
    "plt.text(200,20,'Integral Abs Error: ' + str(np.round(iae,2)))\n",
    "plt.text(400,35,r'$K_p$: ' + str(np.round(Kp,1)))  \n",
    "plt.text(400,30,r'$K_d$: ' + str(np.round(Kd,1)))  \n",
    "plt.text(400,25,r'$\\tau_p$: ' + str(np.round(taup,0)) + ' sec')  \n",
    "plt.text(400,20,r'$\\theta_p$: ' + str(np.round(thetap,0)) + ' sec')  \n",
    "plt.legend(loc=2)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(t,Q1,'b--',linewidth=2,label=r'Heater 1 ($Q_1$)')\n",
    "plt.plot(t,Q2,'r:',linewidth=2,label=r'Heater 2 ($Q_2$)')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('time (sec)')\n",
    "plt.savefig('fopdt_opt.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design 2 PID Controllers for T1 and T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "import ipywidgets as wg\n",
    "from IPython.display import display\n",
    "\n",
    "n = 601 # time points to plot\n",
    "tf = 600.0 # final time\n",
    "\n",
    "# Load TCLab FOPDT\n",
    "fid = open('fopdt.txt','r')\n",
    "Kp = float(fid.readline())\n",
    "Kd = float(fid.readline())\n",
    "taup = float(fid.readline())\n",
    "thetap = float(fid.readline())\n",
    "T1_0 = float(fid.readline())\n",
    "T2_0 = float(fid.readline())\n",
    "fid.close()\n",
    "y0 = [T1_0,T2_0]\n",
    "Kff = -Kp/Kd\n",
    "\n",
    "def process(y,t,u1,u2):\n",
    "    y1,y2 = y\n",
    "    dy1dt = (1.0/taup) * (-(y1-y0[0]) + Kp * u1 + Kd * (y2-y1))\n",
    "    dy2dt = (1.0/taup) * (-(y2-y0[1]) + (Kp/2.0) * u2 + Kd * (y1-y2))\n",
    "    return [dy1dt,dy2dt]\n",
    "\n",
    "def pidPlot(Kc,tauI,tauD,Kff):\n",
    "    y0 = [23.0,23.0]\n",
    "    t = np.linspace(0,tf,n) # create time vector\n",
    "    P1 = np.zeros(n)         # initialize proportional term\n",
    "    I1 = np.zeros(n)         # initialize integral term\n",
    "    D1 = np.zeros(n)         # initialize derivative term\n",
    "    FF1 = np.zeros(n)        # initialize feedforward term\n",
    "    e1 = np.zeros(n)         # initialize error\n",
    "    P2 = np.zeros(n)         # initialize proportional term\n",
    "    I2 = np.zeros(n)         # initialize integral term\n",
    "    D2 = np.zeros(n)         # initialize derivative term\n",
    "    FF2 = np.zeros(n)        # initialize feedforward term\n",
    "    e2 = np.zeros(n)         # initialize error\n",
    "    OP1 = np.zeros(n)       # initialize controller output\n",
    "    OP2 = np.zeros(n)       # initialize disturbance\n",
    "    PV1 = np.ones(n)*y0[0]  # initialize process variable\n",
    "    PV2 = np.ones(n)*y0[1]  # initialize process variable\n",
    "    SP1 = np.ones(n)*y0[0]  # initialize setpoint\n",
    "    SP2 = np.ones(n)*y0[1]  # initialize setpoint\n",
    "    SP1[10:] = 60.0         # step up\n",
    "    SP1[400:] = 40.0         # step up\n",
    "    SP2[150:] = 50.0        # step down\n",
    "    SP2[350:] = 35.0        # step down\n",
    "    Kc1 = Kc\n",
    "    Kc2 = Kc*2.0\n",
    "    Kff1 = Kff\n",
    "    Kff2 = Kff*2.0\n",
    "    iae = 0.0\n",
    "    # loop through all time steps\n",
    "    for i in range(1,n):\n",
    "        # simulate process for one time step\n",
    "        ts = [t[i-1],t[i]]         # time interval\n",
    "        heaters = (OP1[max(0,i-int(thetap))],OP2[max(0,i-int(thetap))])\n",
    "        y = odeint(process,y0,ts,args=heaters)\n",
    "        y0 = y[1]                  # record new initial condition\n",
    "        # calculate new OP with PID\n",
    "        PV1[i] = y[1][0]              # record T1 PV\n",
    "        PV2[i] = y[1][1]              # record T2 PV\n",
    "        iae += np.abs(SP1[i]-PV1[i]) + np.abs(SP2[i]-PV2[i])\n",
    "        dt = t[i] - t[i-1]         # calculate time step\n",
    "        \n",
    "        # PID for loop 1\n",
    "        e1[i] = SP1[i] - PV1[i]       # calculate error = SP - PV\n",
    "        P1[i] = Kc1 * e1[i]           # calculate proportional term\n",
    "        I1[i] = I1[i-1] + (Kc1/tauI) * e1[i] * dt  # calculate integral term\n",
    "        D1[i] = -Kc * tauD * (PV1[i]-PV1[i-1])/dt # calculate derivative\n",
    "        FF1[i] = Kff1 * (PV2[i]-PV1[i])\n",
    "        OP1[i] = P1[i] + I1[i] + D1[i] + FF1[i] # calculate new output\n",
    "        if OP1[i]>=100:\n",
    "            OP1[i] = 100.0\n",
    "            I1[i] = I1[i-1] # reset integral\n",
    "        if OP1[i]<=0:\n",
    "            OP1[i] = 0.0\n",
    "            I1[i] = I1[i-1] # reset integral            \n",
    "\n",
    "        # PID for loop 2\n",
    "        e2[i] = SP2[i] - PV2[i]       # calculate error = SP - PV\n",
    "        P2[i] = Kc2 * e2[i]           # calculate proportional term\n",
    "        I2[i] = I2[i-1] + (Kc2/tauI) * e2[i] * dt  # calculate integral term\n",
    "        D2[i] = -Kc2 * tauD * (PV2[i]-PV2[i-1])/dt # calculate derivative\n",
    "        FF2[i] = Kff2 * (PV1[i]-PV2[i])\n",
    "        OP2[i] = P2[i] + I2[i] + D2[i] + FF2[i] # calculate new output\n",
    "        if OP2[i]>=100:\n",
    "            OP2[i] = 100.0\n",
    "            I2[i] = I2[i-1] # reset integral\n",
    "        if OP2[i]<=0:\n",
    "            OP2[i] = 0.0\n",
    "            I2[i] = I2[i-1] # reset integral            \n",
    "            \n",
    "    # plot PID response\n",
    "    plt.figure(1,figsize=(15,7))\n",
    "    plt.subplot(2,2,1)\n",
    "    plt.plot(t,SP1,'k-',linewidth=2,label='Setpoint 1 (SP)')\n",
    "    plt.plot(t,PV1,'r:',linewidth=2,label='Temperature 1 (PV)')\n",
    "    plt.ylabel(r'T $(^oC)$')\n",
    "    plt.text(100,35,'Integral Abs Error: ' + str(np.round(iae,2)))\n",
    "    plt.text(400,35,r'$K_{c1}$: ' + str(np.round(Kc,1)))  \n",
    "    plt.text(400,30,r'$\\tau_I$: ' + str(np.round(tauI,0)) + ' sec')  \n",
    "    plt.text(400,25,r'$\\tau_D$: ' + str(np.round(tauD,0)) + ' sec')  \n",
    "    plt.text(400,20,r'$K_{ff}$: ' + str(np.round(Kff1,2)))  \n",
    "    plt.ylim([15,70])\n",
    "    plt.legend(loc=1)\n",
    "    plt.subplot(2,2,2)\n",
    "    plt.plot(t,SP2,'k-',linewidth=2,label='Setpoint 2 (SP)')\n",
    "    plt.plot(t,PV2,'r:',linewidth=2,label='Temperature 2 (PV)')\n",
    "    plt.ylabel(r'T $(^oC)$')\n",
    "    plt.text(20,65,r'$K_{c2}$: ' + str(np.round(Kc*2,1)))  \n",
    "    plt.text(20,60,r'$\\tau_I$: ' + str(np.round(tauI,0)) + ' sec')  \n",
    "    plt.text(20,55,r'$\\tau_D$: ' + str(np.round(tauD,0)) + ' sec')  \n",
    "    plt.text(20,50,r'$K_{ff}$: ' + str(np.round(Kff2,2)))\n",
    "    plt.ylim([15,70])\n",
    "    plt.legend(loc=1)\n",
    "    plt.subplot(2,2,3)\n",
    "    plt.plot(t,OP1,'b--',linewidth=2,label='Heater 1 (OP)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('time (sec)')\n",
    "    plt.subplot(2,2,4)\n",
    "    plt.plot(t,OP2,'b--',linewidth=2,label='Heater 2 (OP)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('time (sec)')\n",
    "\n",
    "print('PID with Feedforward Simulator: Adjust Kc, tauI, tauD, and Kff ' + \\\n",
    "      'to achieve lowest Integral Abs Error')\n",
    "\n",
    "# ITAE Setpoint Tracking PI Tuning\n",
    "Kc = (0.586/Kp)*(thetap/taup)**(-0.916); tauI = taup/(1.03-0.165*(thetap/taup))\n",
    "print(f'ITAE Recommended: Kc={Kc:4.2f}, tauI={tauI:5.1f}, tauD=0, Kff={Kff:4.2f}')\n",
    "\n",
    "# IMC Aggressive PID Tuning\n",
    "tauc = max(0.1*taup,0.8*thetap); Kc = (1/Kp)*(taup+0.5*taup)/(tauc+0.5*thetap)\n",
    "tauI = taup+0.5*thetap; tauD = taup*thetap/(2*taup+thetap); Kff=-Kd/Kp\n",
    "print(f'IMC Recommended: Kc={Kc:4.2f}, tauI={tauI:5.1f}, tauD={tauD:4.2f}, Kff={Kff:4.2f}')\n",
    "\n",
    "Kc_slide = wg.FloatSlider(value=Kc,min=0.0,max=50.0,step=1.0)\n",
    "tauI_slide = wg.FloatSlider(value=tauI,min=20.0,max=250.0,step=1.0)\n",
    "tauD_slide = wg.FloatSlider(value=tauD,min=0.0,max=20.0,step=1.0)\n",
    "Kff_slide = wg.FloatSlider(value=Kff,min=-0.5,max=0.5,step=0.1)\n",
    "wg.interact(pidPlot, Kc=Kc_slide, tauI=tauI_slide, tauD=tauD_slide,Kff=Kff_slide)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Interacting PID Controllers with TCLab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tclab\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "\n",
    "#-----------------------------------------\n",
    "# PID controller performance for the TCLab\n",
    "#-----------------------------------------\n",
    "# PID Parameters\n",
    "Kc   = 5.0\n",
    "tauI = 120.0 # sec\n",
    "tauD = 2.0   # sec\n",
    "Kff  = -0.3\n",
    "\n",
    "# Animate Plot?\n",
    "animate = True\n",
    "if animate:\n",
    "    try:\n",
    "        from IPython import get_ipython\n",
    "        from IPython.display import display,clear_output\n",
    "        get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "        ipython = True\n",
    "        print('IPython Notebook')\n",
    "    except:\n",
    "        ipython = False\n",
    "        print('Not IPython Notebook')\n",
    "\n",
    "#-----------------------------------------\n",
    "# PID Controller with Feedforward\n",
    "#-----------------------------------------\n",
    "# inputs ---------------------------------\n",
    "# sp = setpoint\n",
    "# pv = current temperature\n",
    "# pv_last = prior temperature\n",
    "# ierr = integral error\n",
    "# dt = time increment between measurements\n",
    "# outputs --------------------------------\n",
    "# op = output of the PID controller\n",
    "# P = proportional contribution\n",
    "# I = integral contribution\n",
    "# D = derivative contribution\n",
    "def pid(sp,pv,pv_last,ierr,dt,d,cid):\n",
    "    # Parameters in terms of PID coefficients\n",
    "    if cid==1:\n",
    "        # controller 1\n",
    "        KP = Kc\n",
    "        Kf = Kff\n",
    "    else:\n",
    "        # controller 2\n",
    "        KP = Kc * 2.0\n",
    "        Kf = Kff * 2.0\n",
    "    KI = Kc/tauI\n",
    "    KD = Kc*tauD\n",
    "\n",
    "    # ubias for controller (initial heater)\n",
    "    op0 = 0 \n",
    "    # upper and lower bounds on heater level\n",
    "    ophi = 100\n",
    "    oplo = 0\n",
    "    # calculate the error\n",
    "    error = sp-pv\n",
    "    # calculate the integral error\n",
    "    ierr = ierr + KI * error * dt\n",
    "    # calculate the measurement derivative\n",
    "    dpv = (pv - pv_last) / dt\n",
    "    # calculate the PID output\n",
    "    P = KP * error\n",
    "    I = ierr\n",
    "    D = -KD * dpv\n",
    "    FF = Kff * d\n",
    "    op = op0 + P + I + D + FF\n",
    "    # implement anti-reset windup\n",
    "    if op < oplo or op > ophi:\n",
    "        I = I - KI * error * dt\n",
    "        # clip output\n",
    "        op = max(oplo,min(ophi,op))\n",
    "    # return the controller output and PID terms\n",
    "    return [op,P,I,D,FF]\n",
    "\n",
    "# save txt file with data and set point\n",
    "# t = time\n",
    "# u1,u2 = heaters\n",
    "# y1,y2 = tempeatures\n",
    "# sp1,sp2 = setpoints\n",
    "def save_txt(t, u1, u2, y1, y2, sp1, sp2):\n",
    "    data = np.vstack((t, u1, u2, y1, y2, sp1, sp2))  # vertical stack\n",
    "    data = data.T  # transpose data\n",
    "    top = ('Time,Q1,Q2,T1,T2,TSP1,TSP2')\n",
    "    np.savetxt('validate.txt', data, delimiter=',',\\\n",
    "               header=top, comments='')\n",
    "\n",
    "# Connect to Arduino\n",
    "a = tclab.TCLab()\n",
    "\n",
    "# Wait until temperature is below 25 degC\n",
    "print('Check that temperatures are < 25 degC before starting')\n",
    "i = 0\n",
    "while a.T1>=25.0 or a.T2>=25.0:\n",
    "    print(f'Time: {i} T1: {a.T1} T2: {a.T2}')\n",
    "    i += 10\n",
    "    time.sleep(10)\n",
    "\n",
    "# Turn LED on\n",
    "print('LED On')\n",
    "a.LED(100)\n",
    "\n",
    "# Run time in minutes\n",
    "run_time = 10.0\n",
    "\n",
    "# Number of cycles\n",
    "loops = int(60.0*run_time)\n",
    "tm = np.zeros(loops)\n",
    "\n",
    "# Heater set point steps\n",
    "Tsp1 = np.ones(loops) * a.T1\n",
    "Tsp2 = np.ones(loops) * a.T2 # set point (degC)\n",
    "Tsp1[10:] = 60.0         # step up\n",
    "Tsp1[400:] = 40.0        # step down\n",
    "Tsp2[150:] = 50.0        # step up\n",
    "Tsp2[350:] = 35.0        # step down\n",
    "\n",
    "T1 = np.ones(loops) * a.T1 # measured T (degC)\n",
    "T2 = np.ones(loops) * a.T2 # measured T (degC)\n",
    "\n",
    "# impulse tests (0 - 100%)\n",
    "Q1 = np.ones(loops) * 0.0\n",
    "Q2 = np.ones(loops) * 0.0\n",
    "\n",
    "if not animate:\n",
    "    print('Running Main Loop. Ctrl-C to end.')\n",
    "    print('  Time    SP1    PV1     Q1    SP2    PV2     Q2    IAE')\n",
    "    print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "           '{:6.2f} {:6.2f} {:6.2f} {:6.2f} {:6.2f}').format( \\\n",
    "               tm[0],Tsp1[0],T1[0],Q1[0],Tsp2[0],T2[0],Q2[0],0.0))\n",
    "\n",
    "# Main Loop\n",
    "start_time = time.time()\n",
    "prev_time = start_time\n",
    "dt_error = 0.0\n",
    "# Integral error\n",
    "ierr1 = 0.0\n",
    "ierr2 = 0.0\n",
    "# Integral absolute error\n",
    "iae = 0.0\n",
    "\n",
    "if not ipython:\n",
    "    plt.figure(figsize=(10,7))\n",
    "    plt.ion()\n",
    "    plt.show()\n",
    "\n",
    "try:\n",
    "    for i in range(1,loops):\n",
    "        # Sleep time\n",
    "        sleep_max = 1.0\n",
    "        sleep = sleep_max - (time.time() - prev_time) - dt_error\n",
    "        if sleep>=1e-4:\n",
    "            time.sleep(sleep-1e-4)\n",
    "        else:\n",
    "            print('exceeded max cycle time by ' + str(abs(sleep)) + ' sec')\n",
    "            time.sleep(1e-4)\n",
    "\n",
    "        # Record time and change in time\n",
    "        t = time.time()\n",
    "        dt = t - prev_time\n",
    "        if (sleep>=1e-4):\n",
    "            dt_error = dt-sleep_max+0.009\n",
    "        else:\n",
    "            dt_error = 0.0\n",
    "        prev_time = t\n",
    "        tm[i] = t - start_time\n",
    "\n",
    "        # Read temperatures in Kelvin \n",
    "        T1[i] = a.T1\n",
    "        T2[i] = a.T2\n",
    "\n",
    "        # Disturbances\n",
    "        d1 = T1[i] - 23.0\n",
    "        d2 = T2[i] - 23.0\n",
    "\n",
    "        # Integral absolute error\n",
    "        iae += np.abs(Tsp1[i]-T1[i]) + np.abs(Tsp2[i]-T2[i])\n",
    "\n",
    "        # Calculate PID output\n",
    "        [Q1[i],P,ierr1,D,FF] = pid(Tsp1[i],T1[i],T1[i-1],ierr1,dt,d2,1)\n",
    "        [Q2[i],P,ierr2,D,FF] = pid(Tsp2[i],T2[i],T2[i-1],ierr2,dt,d1,2)\n",
    "\n",
    "        # Write output (0-100)\n",
    "        a.Q1(Q1[i])\n",
    "        a.Q2(Q2[i])\n",
    "\n",
    "        if not animate:\n",
    "            # Print line of data\n",
    "            print(('{:6.1f} {:6.2f} {:6.2f} ' + \\\n",
    "                  '{:6.2f} {:6.2f} {:6.2f} {:6.2f} {:6.2f}').format( \\\n",
    "                      tm[i],Tsp1[i],T1[i],Q1[i],Tsp2[i],T2[i],Q2[i],iae))\n",
    "        else:\n",
    "            if ipython:\n",
    "                plt.figure(figsize=(10,7))\n",
    "            else:\n",
    "                plt.clf()\n",
    "            # Update plot\n",
    "            ax=plt.subplot(2,1,1)\n",
    "            ax.grid()\n",
    "            plt.plot(tm[0:i],Tsp1[0:i],'k--',label=r'$T_1$ set point')\n",
    "            plt.plot(tm[0:i],T1[0:i],'b.',label=r'$T_1$ measured')\n",
    "            plt.plot(tm[0:i],Tsp2[0:i],'k-',label=r'$T_2$ set point')\n",
    "            plt.plot(tm[0:i],T2[0:i],'r.',label=r'$T_2$ measured')\n",
    "            plt.ylabel(r'Temperature ($^oC$)')\n",
    "            plt.text(0,65,'IAE: ' + str(np.round(iae,2)))\n",
    "            plt.legend(loc=4)\n",
    "            plt.ylim([15,70])\n",
    "            ax=plt.subplot(2,1,2)\n",
    "            ax.grid()\n",
    "            plt.plot(tm[0:i],Q1[0:i],'b-',label=r'$Q_1$')\n",
    "            plt.plot(tm[0:i],Q2[0:i],'r:',label=r'$Q_2$')\n",
    "            plt.ylim([-10,110])\n",
    "            plt.ylabel('Heater (%)')\n",
    "            plt.legend(loc=1)\n",
    "            plt.xlabel('Time (sec)')\n",
    "            if ipython:\n",
    "                clear_output(wait=True)\n",
    "                display(plt.gcf())\n",
    "            else:\n",
    "                plt.draw()\n",
    "                plt.pause(0.05)\n",
    "\n",
    "    # Turn off heaters\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    a.close()\n",
    "    # Save text file\n",
    "    save_txt(tm,Q1,Q2,T1,T2,Tsp1,Tsp2)\n",
    "    # Save Plot\n",
    "    if not animate:\n",
    "        plt.figure(figsize=(10,7))\n",
    "        ax=plt.subplot(2,1,1)\n",
    "        ax.grid()\n",
    "        plt.plot(tm,Tsp1,'k--',label=r'$T_1$ set point')\n",
    "        plt.plot(tm,T1,'b.',label=r'$T_1$ measured')\n",
    "        plt.plot(tm,Tsp2,'k-',label=r'$T_2$ set point')\n",
    "        plt.plot(tm,T2,'r.',label=r'$T_2$ measured')\n",
    "        plt.ylabel(r'Temperature ($^oC$)')\n",
    "        plt.text(0,65,'IAE: ' + str(np.round(iae,2)))\n",
    "        plt.legend(loc=4)\n",
    "        ax=plt.subplot(2,1,2)\n",
    "        ax.grid()\n",
    "        plt.plot(tm,Q1,'b-',label=r'$Q_1$')\n",
    "        plt.plot(tm,Q2,'r:',label=r'$Q_2$')\n",
    "        plt.ylabel('Heater (%)')\n",
    "        plt.legend(loc=1)\n",
    "        plt.xlabel('Time (sec)')\n",
    "    plt.savefig('PID_Control.png')\n",
    "\n",
    "# Allow user to end loop with Ctrl-C           \n",
    "except KeyboardInterrupt:\n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Shutting down')\n",
    "    a.close()\n",
    "    save_txt(tm[0:i],Q1[0:i],Q2[0:i],T1[0:i],T2[0:i],Tsp1[0:i],Tsp2[0:i])\n",
    "    plt.savefig('PID_Control.png')\n",
    "\n",
    "# Make sure serial connection closes with an error\n",
    "except:           \n",
    "    # Disconnect from Arduino\n",
    "    a.Q1(0)\n",
    "    a.Q2(0)\n",
    "    print('Error: Shutting down')\n",
    "    a.close()\n",
    "    save_txt(tm[0:i],Q1[0:i],Q2[0:i],T1[0:i],T2[0:i],Tsp1[0:i],Tsp2[0:i])\n",
    "    plt.savefig('PID_Control.png')\n",
    "    raise\n",
    "\n",
    "print('PID test complete')\n",
    "print('Kc: ' + str(Kc))\n",
    "print('tauI: ' + str(tauI))\n",
    "print('tauD: ' + str(tauD))\n",
    "print('Kff: ' + str(Kff))\n"
   ]
  }
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