{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html)", "section": "" } }, "source": [ "\n", "*This notebook contains material from [CBE32338](https://jckantor.github.io/CBE32338);\n", "content is available [on Github](https://github.com/jckantor/CBE32338.git).*\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html)", "section": "" } }, "source": [ "\n", "< [5.1 Simulation, Control, and Estimation using Pyomo](https://jckantor.github.io/CBE32338/05.01-Optimization-Control-and-Estimation-using-Pyomo.html) | [Contents](toc.html) | [A.0 Additional Python](https://jckantor.github.io/CBE32338/A.00-Additional-Python.html) >

\"Open

\"Download\"" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 1, "link": "[5.2 Simulation, Control, and Estimation using Pyomo](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2-Simulation,-Control,-and-Estimation-using-Pyomo)", "section": "5.2 Simulation, Control, and Estimation using Pyomo" } }, "source": [ "# 5.2 Simulation, Control, and Estimation using Pyomo" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.1 Installations](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1-Installations)", "section": "5.2.1 Installations" } }, "source": [ "## 5.2.1 Installations\n", "\n", "The following instructions show how to download and install pyomo and the ipopt solver. Execute the appropriate cell for your platform (if needed)." ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.1 Google Colab](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.1-Google-Colab)", "section": "5.2.1.1 Google Colab" } }, "source": [ "### 5.2.1.1 Google Colab" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.1 Google Colab](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.1-Google-Colab)", "section": "5.2.1.1 Google Colab" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/sh: wget: command not found\n", "unzip: cannot find or open ipopt-linux64, ipopt-linux64.zip or ipopt-linux64.ZIP.\n" ] } ], "source": [ "!pip install -q pyomo\n", "!wget -N -q \"https://ampl.com/dl/open/ipopt/ipopt-linux64.zip\"\n", "!unzip -o -q ipopt-linux64\n", "ipopt_executable = '/content/ipopt'" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.2 MacOS](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.2-MacOS)", "section": "5.2.1.2 MacOS" } }, "source": [ "### 5.2.1.2 MacOS" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.2 MacOS](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.2-MacOS)", "section": "5.2.1.2 MacOS" } }, "outputs": [], "source": [ "!pip install -q pyomo\n", "!curl -s https://ampl.com/dl/open/ipopt/ipopt-osx.zip --output ipopt-osx.zip\n", "!tar xf ipopt-osx.zip ipopt\n", "ipopt_executable = \"./ipopt\"\n", "!rm ipopt-osx.zip" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.3 Windows PC](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.3-Windows-PC)", "section": "5.2.1.3 Windows PC" } }, "source": [ "### 5.2.1.3 Windows PC" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "nbpages": { "level": 3, "link": "[5.2.1.3 Windows PC](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.1.3-Windows-PC)", "section": "5.2.1.3 Windows PC" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting package metadata: ...working... done\n", "Solving environment: ...working... done\n", "\n", "# All requested packages already installed.\n", "\n", "Collecting package metadata: ...working... done\n", "Solving environment: ...working... done\n", "\n", "# All requested packages already installed.\n", "\n" ] } ], "source": [ "!conda install -c conda-forge pyomo pyomo.extras\n", "!conda install -c conda-forge/label/cf201901 ipopt " ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.2 Process Information](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2-Process-Information)", "section": "5.2.2 Process Information" } }, "source": [ "## 5.2.2 Process Information\n", "\n", "\\begin{align*}\n", "C_p^H \\frac{dT_H}{dt} & = U_a (T_{amb} - T_H) + U_c (T_S - T_H) + P u(t) + d(t)\\\\\n", "C_p^S \\frac{dT_S}{dt} & = - U_c (T_S - T_H) \n", "\\end{align*}\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.2.1 Process Parameter Values](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.1-Process-Parameter-Values)", "section": "5.2.2.1 Process Parameter Values" } }, "source": [ "### 5.2.2.1 Process Parameter Values" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbpages": { "level": 3, "link": "[5.2.2.1 Process Parameter Values](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.1-Process-Parameter-Values)", "section": "5.2.2.1 Process Parameter Values" } }, "outputs": [], "source": [ "P = 0.04 # power input when the system is turned\n", "Ua = 0.068 # heat transfer coefficient from heater to environment\n", "CpH = 6.50 # heat capacity of the heater (J/deg C)\n", "CpS = 1.25 # heat capacity of the sensor (J/deg C)\n", "Uc = 0.036 # heat transfer coefficient from heater to sensor\n", "Tamb = 21.0 # ambient room temperature" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.2.2 Process Inputs](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.2-Process-Inputs)", "section": "5.2.2.2 Process Inputs" } }, "source": [ "### 5.2.2.2 Process Inputs\n", "\n", "The next cell defines some process inputs that will be used throughout the notebook to demonstrate aspects of process simulation, control, and estimation. These are gathered in one place to make it easier to modify the notebook to test the response under different conditions. These functions are implemented using the `interp1d` from the `scipy` library." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "nbpages": { "level": 3, "link": "[5.2.2.2 Process Inputs](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.2.2-Process-Inputs)", "section": "5.2.2.2 Process Inputs" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from scipy import interpolate\n", "import numpy as np\n", "\n", "tclab_disturbance = interpolate.interp1d(\n", " [ 0, 300, 400, 9999], \n", " [ 0, 0, -.5, -.5])\n", "\n", "tclab_input = interpolate.interp1d(\n", " [ 0, 50, 51, 450, 451, 9999],\n", " [ 0, 0, 80, 80, 25, 25])\n", "\n", "tclab_setpoint = interpolate.interp1d(\n", " [0, 50, 150, 450, 550, 9999], \n", " [Tamb, Tamb, 60, 60, 35, 35])\n", "\n", "t_sim = np.linspace(0, 1000, 201)\n", "u_sim = tclab_input(t_sim)\n", "d_sim = tclab_disturbance(t_sim)\n", "setpoint_sim = tclab_setpoint(t_sim)\n", "\n", "plt.figure(figsize=(10,8))\n", "plt.subplot(3, 1, 1)\n", "plt.plot(t_sim, setpoint_sim)\n", "plt.title('setpoint')\n", "plt.ylabel('deg C')\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.plot(t_sim, u_sim)\n", "plt.title('heat power input')\n", "plt.ylabel('percent of max')\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.plot(t_sim, d_sim)\n", "plt.title('unmeasured disturbance')\n", "plt.ylabel('watts')\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.3 Pyomo Simulation](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.3-Pyomo-Simulation)", "section": "5.2.3 Pyomo Simulation" } }, "source": [ "## 5.2.3 Pyomo Simulation\n", "\n", "Let's see how well our initial guess at a control strategy will work for us.\n", "\n", "\\begin{align*}\n", "C_p^H \\frac{dT_H}{dt} & = U_a (T_{amb} - T_H) + U_c (T_S - T_H) + P u(t) + d(t)\\\\\n", "C_p^S \\frac{dT_S}{dt} & = - U_c (T_S - T_H) \n", "\\end{align*}\n", "\n", "subject to initial conditions\n", "\n", "\\begin{align*}\n", "T_H(t_0) & = T_{amb} \\\\\n", "T_S(t_0) & = T_{amb}\n", "\\end{align*}\n", "\n", "and prior specification of inputs $u(t)$ and $d(t)$." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "nbpages": { "level": 2, "link": "[5.2.3 Pyomo Simulation](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.3-Pyomo-Simulation)", "section": "5.2.3 Pyomo Simulation" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: More finite elements were found in ContinuousSet 't' than the number\n", " of finite elements specified in apply. The larger number of finite\n", " elements will be used.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "\n", "m = ConcreteModel()\n", "m.t = ContinuousSet(initialize = t_sim) # make sure the expt time grid are discretization points\n", "m.Th = Var(m.t)\n", "m.Ts = Var(m.t)\n", "m.U = Var(m.t, bounds=(0, 100))\n", "m.D = Var(m.t)\n", "\n", "m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", "m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", "\n", "# differential equations\n", "m.Th_ode = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + m.D[t])\n", "m.Ts_ode = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", "\n", "# input specifications\n", "m.Usim = Constraint(range(0, len(t_sim)), rule = lambda m, k: m.U[t_sim[k]] == u_sim[k])\n", "m.Dsim = Constraint(range(0, len(t_sim)), rule = lambda m, k: m.D[t_sim[k]] == d_sim[k])\n", "\n", "# initial conditions\n", "m.Th[0].fix(Tamb)\n", "m.Ts[0].fix(Tamb)\n", "\n", "TransformationFactory('dae.finite_difference').apply_to(m, method='forward')\n", "SolverFactory('ipopt').solve(m)\n", "\n", "Th_sim = np.array([m.Th[t]() for t in t_sim])\n", "Ts_sim = np.array([m.Ts[t]() for t in t_sim])\n", "\n", "# visualization\n", "plt.figure(figsize=(10,8))\n", "plt.subplot(3, 1, 1)\n", "plt.plot(t_sim, Th_sim)\n", "plt.plot(t_sim, Ts_sim)\n", "plt.plot(t_sim, setpoint_sim)\n", "plt.title('temperatures')\n", "plt.ylabel('deg C')\n", "plt.legend(['T_heater', 'T_sensor', 'Heater Setpoint'])\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.plot(t_sim, np.array([m.U[t]() for t in t_sim]))\n", "plt.title('heater power')\n", "plt.ylabel('percent of max')\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.plot(t_sim, np.array([m.D[t]() for t in t_sim]))\n", "plt.title('disturbance')\n", "plt.ylabel('watts')\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.4 Optimal Control with Knowledge of Disturbances](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.4-Optimal-Control-with-Knowledge-of-Disturbances)", "section": "5.2.4 Optimal Control with Knowledge of Disturbances" } }, "source": [ "## 5.2.4 Optimal Control with Knowledge of Disturbances\n", "\n", "An optimal control policy minimizes the differences\n", "\n", "\\begin{align*}\n", "\\min_{u} \\int_{t_0}^{t_f} \\|T_H^{SP}(t) - T_H(t)\\|^2\\,dt \\\\\n", "\\end{align*}\n", "\n", "subject to constraints\n", "\n", "\\begin{align*}\n", "C_p^H \\frac{dT_H}{dt} & = U_a (T_{amb} - T_H) + U_c (T_S - T_H) + P u(t) + d(t)\\\\\n", "C_p^S \\frac{dT_S}{dt} & = - U_c (T_S - T_H) \n", "\\end{align*}\n", "\n", "initial conditions\n", "\n", "\\begin{align*}\n", "T_H(t_0) & = T_{amb} \\\\\n", "T_S(t_0) & = T_{amb}\n", "\\end{align*}\n", "\n", "and prior knowledge of $d(t)$." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "nbpages": { "level": 2, "link": "[5.2.4 Optimal Control with Knowledge of Disturbances](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.4-Optimal-Control-with-Knowledge-of-Disturbances)", "section": "5.2.4 Optimal Control with Knowledge of Disturbances" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# ==========================================================\n", "# = Solver Results =\n", "# ==========================================================\n", "# ----------------------------------------------------------\n", "# Problem Information\n", "# ----------------------------------------------------------\n", "Problem: \n", "- Lower bound: -inf\n", " Upper bound: inf\n", " Number of objectives: 1\n", " Number of constraints: 1007\n", " Number of variables: 1210\n", " Sense: unknown\n", "# ----------------------------------------------------------\n", "# Solver Information\n", "# ----------------------------------------------------------\n", "Solver: \n", "- Status: ok\n", " Message: Ipopt 3.11.1\\x3a Optimal Solution Found\n", " Termination condition: optimal\n", " Id: 0\n", " Error rc: 0\n", " Time: 0.27823472023010254\n", "# ----------------------------------------------------------\n", "# Solution Information\n", "# ----------------------------------------------------------\n", "Solution: \n", "- number of solutions: 0\n", " number of solutions displayed: 0\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#%%time #if you put this as the first line, jupyter will report how long the code took to execute. \n", "\n", "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "m = ConcreteModel()\n", "m.t = ContinuousSet(initialize = t_sim) # make sure the expt time grid are discretization points\n", "m.Th = Var(m.t)\n", "m.Ts = Var(m.t)\n", "m.U = Var(m.t, bounds=(0, 100))\n", "m.D = Var(m.t)\n", "\n", "m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", "m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", "\n", "# differential equations\n", "m.Th_ode = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + m.D[t])\n", "m.Ts_ode = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", "\n", "# input specifications\n", "m.Dsim = Constraint(range(0, len(t_sim)), rule = lambda m, k: m.D[t_sim[k]] == d_sim[k])\n", "\n", "# with these two lines which provide an objective function to determine the input\n", "m.ls_control = sum([(setpoint_sim[k] - m.Th[t_sim[k]])**2 for k in range(0, len(t_sim))])\n", "m.obj = Objective(expr = m.ls_control, sense=minimize)\n", "\n", "# initial conditions\n", "m.Th[0].fix(Tamb)\n", "m.Ts[0].fix(Tamb)\n", "\n", "TransformationFactory('dae.finite_difference').apply_to(m, nfe=len(t_sim), method='forward')\n", "SolverFactory('ipopt').solve(m).write()\n", "\n", "# visualization\n", "plt.figure(figsize=(10,8))\n", "plt.subplot(3, 1, 1)\n", "plt.plot(t_sim, np.array([m.Th[t]() for t in t_sim]))\n", "plt.plot(t_sim, np.array([m.Ts[t]() for t in t_sim]))\n", "plt.plot(t_sim, setpoint_sim)\n", "plt.title('temperatures')\n", "plt.ylabel('deg C')\n", "plt.legend(['T_heater', 'T_sensor', 'Heater Setpoint'])\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.plot(t_sim, np.array([m.U[t]() for t in t_sim]))\n", "plt.title('heater power')\n", "plt.ylabel('percent of max')\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.plot(t_sim, np.array([m.D[t]() for t in t_sim]))\n", "plt.title('disturbance')\n", "plt.ylabel('watts')\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 3, "link": "[5.2.4.1 Exercise](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.4.1-Exercise)", "section": "5.2.4.1 Exercise" } }, "source": [ "### 5.2.4.1 Exercise\n", "\n", "The optimal control computed above requires rapid changes in power level. In process systems where control action requires movement of a valve stem position, there are often limits on how fast the manipulated variable can change. Modify the model to include differential inequalities that limit the time rate of change of control.\n", "\n", "\\begin{align*}\n", "\\frac{du}{dt} & \\leq \\dot{u}_{max} \\\\\n", "\\frac{du}{dt} & \\geq -\\dot{u}_{max}\n", "\\end{align*}\n", "\n", "where $\\dot{u}_{max}$ is the maximum rate of change. " ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.5 Estimation/Observation](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.5-Estimation/Observation)", "section": "5.2.5 Estimation/Observation" } }, "source": [ "## 5.2.5 Estimation/Observation\n", "\n", "> \"... and now my watch begins ...\" ―The Night's Watch oath, Game of Thrones\n", "\n", "The trouble with open-loop optimal control is that we can't anticipate or know the values of unmeasured disturbances, much less the future values of those disturbances. The best we can do is use available data and process models to estimate the process state and disturbances. The est\n", "\n", "\n", "\\begin{align*}\n", "\\min_{\\hat{T}_H, \\hat{T}_S, \\hat{d}} \\int_{t - h}^t \\|\\hat{T}_S(t) - T_S(t)\\|^2\\,dt \\\\\n", "\\end{align*}\n", "\n", "subject to\n", "\n", "\\begin{align*}\n", "C_p^H \\frac{d\\hat{T}_H}{dt} & = U_a (T_{amb} - \\hat{T}_H) + U_c (\\hat{T}_S - \\hat{T}_H) + P u(t) + \\hat{d}(t)\\\\\n", "C_p^S \\frac{d\\hat{T}_S}{dt} & = - U_c (\\hat{T}_S - \\hat{T}_H) \n", "\\end{align*}\n", "\n", "and initial conditions\n", "\n", "\\begin{align*}\n", "T_H(t_0) & = T_{amb} \\\\\n", "T_S(t_0) & = T_{amb}\n", "\\end{align*}\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "nbpages": { "level": 2, "link": "[5.2.5 Estimation/Observation](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.5-Estimation/Observation)", "section": "5.2.5 Estimation/Observation" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# ==========================================================\n", "# = Solver Results =\n", "# ==========================================================\n", "# ----------------------------------------------------------\n", "# Problem Information\n", "# ----------------------------------------------------------\n", "Problem: \n", "- Lower bound: -inf\n", " Upper bound: inf\n", " Number of objectives: 1\n", " Number of constraints: 1007\n", " Number of variables: 1210\n", " Sense: unknown\n", "# ----------------------------------------------------------\n", "# Solver Information\n", "# ----------------------------------------------------------\n", "Solver: \n", "- Status: ok\n", " Message: Ipopt 3.11.1\\x3a Optimal Solution Found\n", " Termination condition: optimal\n", " Id: 0\n", " Error rc: 0\n", " Time: 0.21533441543579102\n", "# ----------------------------------------------------------\n", "# Solution Information\n", "# ----------------------------------------------------------\n", "Solution: \n", "- number of solutions: 0\n", " number of solutions displayed: 0\n", "Wall time: 718 ms\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%time \n", "\n", "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "m = ConcreteModel()\n", "m.t = ContinuousSet(initialize = t_sim) # make sure the expt time grid are discretization points\n", "m.Th = Var(m.t)\n", "m.Ts = Var(m.t)\n", "m.U = Var(m.t, bounds=(0, 100))\n", "m.D = Var(m.t)\n", "\n", "m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", "m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", "\n", "# differential equations\n", "m.Th_ode = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + m.D[t])\n", "m.Ts_ode = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", "\n", "m.Usim = Constraint(range(0, len(t_sim)), rule = lambda m, k: m.U[t_sim[k]] == u_sim[k])\n", "#for k in range(0, len(t_sim)):\n", "# m.U[t_sim[k]]\n", "\n", "m.ls_observer = sum([(m.Ts[t_sim[k]] - Ts_sim[k])**2 for k in range(0, len(t_sim))])\n", "m.obj = Objective(expr = m.ls_observer, sense=minimize)\n", "\n", "m.Th[0].fix(Tamb)\n", "m.Ts[0].fix(Tamb)\n", "\n", "TransformationFactory('dae.finite_difference').apply_to(m, nfe=len(t_sim), method='forward')\n", "SolverFactory('ipopt').solve(m).write()\n", "\n", "# visualization\n", "plt.figure(figsize=(10,8))\n", "plt.subplot(3, 1, 1)\n", "plt.plot(t_sim, np.array([m.Th[t]() for t in t_sim]))\n", "plt.plot(t_sim, np.array([m.Ts[t]() for t in t_sim]))\n", "plt.plot(t_sim, setpoint_sim)\n", "plt.title('temperatures')\n", "plt.ylabel('deg C')\n", "plt.legend(['T_heater', 'T_sensor', 'Heater Setpoint'])\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.plot(t_sim, np.array([m.U[t]() for t in t_sim]))\n", "plt.title('heater power')\n", "plt.ylabel('percent of max')\n", "plt.grid(True)\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.plot(t_sim, np.array([m.D[t]() for t in t_sim]))\n", "plt.title('disturbance')\n", "plt.ylabel('watts')\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.6 Coding the Observer as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.6-Coding-the-Observer-as-a-Python-Generator)", "section": "5.2.6 Coding the Observer as a Python Generator" } }, "source": [ "## 5.2.6 Coding the Observer as a Python Generator" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "nbpages": { "level": 2, "link": "[5.2.6 Coding the Observer as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.6-Coding-the-Observer-as-a-Python-Generator)", "section": "5.2.6 Coding the Observer as a Python Generator" }, "scrolled": false }, "outputs": [], "source": [ "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "def tclab_observer(h=2):\n", " t_hist = [-1]\n", " u_hist = [0]\n", " Ts_hist = [Tamb]\n", " \n", " t_est = -1\n", " Th_est = []\n", " Ts_est = []\n", " d_est = []\n", " \n", " while True:\n", " t_meas, u_meas, Ts_meas = yield t_est, Th_est, Ts_est, d_est\n", " \n", " t_hist.append(t_meas)\n", " u_hist.append(u_meas)\n", " Ts_hist.append(Ts_meas)\n", " \n", " t_hist = t_hist[-h:]\n", " u_hist = u_hist[-h:]\n", " Ts_hist = Ts_hist[-h:]\n", " \n", " m = ConcreteModel()\n", " m.t = ContinuousSet(initialize = t_hist)\n", " m.Th = Var(m.t)\n", " m.Ts = Var(m.t)\n", " m.U = Var(m.t, bounds=(0, 100))\n", " m.D = Var(m.t)\n", "\n", " m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", " m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", "\n", " m.Th_ode = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + m.D[t])\n", " m.Ts_ode = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", "\n", " m.Usim = Constraint(range(0, len(t_hist)), rule = lambda m, k: m.U[t_hist[k]] == u_hist[k])\n", "\n", " m.ls_observer = sum([(m.Ts[t_hist[k]] - Ts_hist[k])**2 for k in range(0, len(t_hist))])\n", " m.obj = Objective(expr = m.ls_observer, sense=minimize)\n", "\n", " TransformationFactory('dae.finite_difference').apply_to(m, nfe=len(t_hist), method='forward')\n", " SolverFactory('ipopt').solve(m)\n", " \n", " t_est = t_hist[-1]\n", " Th_est = m.Th[t_est]()\n", " Ts_est = m.Ts[t_est]()\n", " d_est = m.D[t_est]()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "nbpages": { "level": 2, "link": "[5.2.6 Coding the Observer as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.6-Coding-the-Observer-as-a-Python-Generator)", "section": "5.2.6 Coding the Observer as a Python Generator" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.53 s, sys: 1.37 s, total: 3.9 s\n", "Wall time: 6.29 s\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%time\n", "\n", "t_est = []\n", "Th_est = []\n", "Ts_est = []\n", "d_est = []\n", "\n", "observer = tclab_observer(5)\n", "observer.send(None)\n", "\n", "for k in range(0, len(t_sim)):\n", " t, Th, Ts, d = observer.send([t_sim[k], u_sim[k], Ts_sim[k]])\n", " t_est.append(t)\n", " Th_est.append(Th)\n", " Ts_est.append(Ts)\n", " d_est.append(d)\n", " \n", "plt.figure(figsize=(10,8))\n", "plt.subplot(3,1,1)\n", "plt.plot(t_est, Th_est)\n", "plt.plot(t_sim, Th_sim)\n", "plt.grid(True)\n", "\n", "plt.subplot(3,1,2)\n", "plt.plot(t_est, Ts_est)\n", "plt.plot(t_sim, Ts_sim)\n", "plt.grid(True)\n", "\n", "plt.subplot(3,1,3)\n", "plt.plot(t_est, d_est)\n", "plt.plot(t_sim, d_sim)\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "source": [ "## 5.2.7 Coding the Controller as a Python Generator" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\eliza\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " del sys.path[0]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pyomo.environ import *\n", "from pyomo.dae import *\n", "\n", "def tclab_control(h=100):\n", " \n", " u = 0\n", " \n", " while True:\n", " t_est, Th_est, Ts_est, d_est = yield u\n", " \n", " tf = t_est + h\n", " m = ConcreteModel()\n", " m.t = ContinuousSet(initialize=np.linspace(t, tf, 1 + round((tf-t)/2)))\n", " m.Th = Var(m.t)\n", " m.Ts = Var(m.t)\n", " m.U = Var(m.t, bounds=(0, 100))\n", " m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", " m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", " m.heater = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + d)\n", " m.sensor = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", " m.Th[t].fix(Th_est)\n", " m.Ts[t].fix(Ts_est)\n", " m.obj = Objective(expr = sum([(tclab_setpoint(t) - m.Th[t])**2 for t in m.t]), sense=minimize)\n", " TransformationFactory('dae.finite_difference').apply_to(m, nfe=len(m.t), method='backwards')\n", " SolverFactory('ipopt').solve(m)\n", " \n", " umpc = np.array([m.U[t]() for t in m.t])\n", " u = umpc[1]\n", "\n", "observer = tclab_observer(3)\n", "observer.send(None)\n", "\n", "controller = tclab_control()\n", "controller.send(None)\n", "\n", "\n", "t_mpc = []\n", "u_mpc = []\n", "Th_mpc = []\n", "Ts_mpc = []\n", "\n", "\n", "for k in range(0, len(t_sim)):\n", " t, Th, Ts, d = observer.send([t_sim[k], u_sim[k], Ts_sim[k]])\n", " u = controller.send([t, Th, Ts, d])\n", " u_mpc.append(u)\n", " Th_mpc.append(Th)\n", " Ts_mpc.append(Ts)\n", " \n", "plt.plot(t_sim, u_mpc)\n", "plt.plot(t_sim, Th_mpc)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "outputs": [ { "data": { "text/plain": [ "201" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(t_sim)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def tclab_control(t, h, ic, d):\n", " tf = t + h\n", " \n", " m = ConcreteModel()\n", " m.t = ContinuousSet(initialize=np.linspace(t, tf, 1 + round((tf-t)/2)))\n", " m.Th = Var(m.t)\n", " m.Ts = Var(m.t)\n", " m.U = Var(m.t, bounds=(0, 100))\n", " m.Thdot = DerivativeVar(m.Th, wrt = m.t)\n", " m.Tsdot = DerivativeVar(m.Ts, wrt = m.t)\n", " m.heater = Constraint(m.t, rule = lambda m, t: \n", " CpH*m.Thdot[t] == Ua*(Tamb - m.Th[t]) + Uc*(m.Ts[t] - m.Th[t]) + P*m.U[t] + d)\n", " m.sensor = Constraint(m.t, rule = lambda m, t: \n", " CpS*m.Tsdot[t] == Uc*(m.Th[t] - m.Ts[t]))\n", " m.Th[t].fix(ic[0])\n", " m.Ts[t].fix(ic[1])\n", " m.obj = Objective(expr = sum([(tclab_setpoint(t) - m.Th[t])**2 for t in m.t]), sense=minimize)\n", " TransformationFactory('dae.finite_difference').apply_to(m, nfe=len(m.t), method='backwards')\n", " SolverFactory('ipopt').solve(m)\n", " tsim = np.array([t for t in m.t])\n", " Thsim = np.array([m.Th[t]() for t in m.t])\n", " Tssim = np.array([m.Ts[t]() for t in m.t])\n", " Usim = np.array([m.U[t]() for t in m.t])\n", " return [tsim, Usim, Thsim, Tssim]\n", " \n", "[tsim, Usim, Thsim, Tssim] = tclab_control(100, 800, [30, 30], .5)\n", " \n", "# visualization\n", "\n", "plt.subplot(2,1,1)\n", "plt.plot(tsim, Thsim, tsim, Tssim)\n", "plt.title('temperatures')\n", "plt.ylabel('deg C')\n", "plt.legend(['T_heater', 'T_sensor'])\n", "plt.grid(True)\n", "\n", "plt.subplot(2,1,2)\n", "plt.plot(tsim, Usim)\n", "plt.title('heater input')\n", "plt.ylabel('percent of max')\n", "plt.grid(True)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "TCLab Model disconnected successfully.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from tclab import setup, clock, Historian, Plotter\n", "TCLab = setup(connected=False, speedup=20)\n", "\n", "tf = 800 # run time\n", "\n", "# create a controller instance\n", "controller = tclab_control(100)\n", "controller.send(None)\n", "\n", "# create an model estimator\n", "observer = tclab_observer(5)\n", "observer.send(None)\n", "\n", "# execute the event loop\n", "tf = 800\n", "with TCLab() as lab:\n", " h = Historian([('T1', lambda: lab.T1), ('Q1', lab.Q1),\n", " ('Th', lambda: Th), ('Ts', lambda: Ts)])\n", " p = Plotter(h, tf)\n", " U1 = 0\n", " for t in clock(tf, 5): # allow time for more calculations\n", " T1 = lab.T1 # measure the sensor temperature\n", " t, Th, Ts, d = observer.send([t, U1, T1]) # estimate the heater temperature # get setpoint\n", " U1 = controller.send([t, Th, Ts, d]) # compute control action\n", " lab.U1 = U1 # set manipulated variable \n", " p.update(t) # log data\n" ] }, { "cell_type": "markdown", "metadata": { "nbpages": { "level": 2, "link": "[5.2.7 Coding the Controller as a Python Generator](https://jckantor.github.io/CBE32338/05.02-Optimization-Control-and-Estimation-using-Pyomo-With-Windows-ipopt.html#5.2.7-Coding-the-Controller-as-a-Python-Generator)", "section": "5.2.7 Coding the Controller as a Python Generator" } }, "source": [ "\n", "< [5.1 Simulation, Control, and Estimation using Pyomo](https://jckantor.github.io/CBE32338/05.01-Optimization-Control-and-Estimation-using-Pyomo.html) | [Contents](toc.html) | [A.0 Additional Python](https://jckantor.github.io/CBE32338/A.00-Additional-Python.html) >

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\"Download\"" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }