{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "*This notebook contains course material from [CBE32338](https://jckantor.github.io/CBE32338)\n", "by Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE2338.git).\n", "The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n", "and code is released under the [MIT license](https://opensource.org/licenses/MIT).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [State Estimation](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/03.00-State-Estimation.ipynb) | [Contents](toc.ipynb) | [Feedback Control](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/04.00-Feedback-Control.ipynb) >

\"Open

\"Download\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Open and Closed Loop Estimation\n", "\n", "This notebook outlines three experiments to be performed using the TCLab hardware. This first cell imports necessary libraries, defines a relay control as a Python generator, and a temperature setpoint corresponding to a ramp/soak cycle." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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mRSk7VcAlRE3N8cZVV9apcVU6VMCLVEmJsXxRRD1RJDS7DnTzfFsXa9W4Km0q4EVs9CTHIyM6ybFMvabmVipKS9S4ahJUwItYLFpD38Aw+4/2hR1FiszpoWEe3tbO25bXMruyIuw4eUsFvIgt10SmhOQHOw9x/OQga7Xv96SogBexy2qrKS81FXCZcuubW4nWTOemS+aFHSWvqYAXsYqyEi6rrdZEpkypjhOn+NEeNa7KBBXwIheLRtjR0T3a+10k6zZubcMd1mjf70lTAS9ysWgNx/oG6OzuDzuKFIF446pW3nTxXBbPUeOqyVIBL3INdfGJTHUmlKnw81eO0nrslBpXZYgKeJFbtjCCmU5yLFOjqbmV6ull/FJMjasyQQW8yFVOK+PCeZXaE0WyruvUIN9p6eSuFXVqXJUhKuBCLFqjniiSdY+qcVXGqYALDdEI7SdOcbxvIOwoUsCatrRyxaLImXkXmTwVcCEWjXeC0zCKZMvOjm5eaO9i7cp6Na7KIBVwSegNrolMyY7RxlV3rVDjqkxSARdmV1ZQN2uGtsAlK04PDfPw9nbeFlPjqkxTARcg3tiqRVvgkgXf33mQEycHuUeTlxmnAi5AfBhl35E++k4PhR1FCsz6La3UzZrBjWpclXEq4ALEJzLd4cVODaNI5rSfOMWP9x7hvWpclRUq4ALokHrJjo3NQeOqxvqwoxQkFXABYGFkOnMqK7QnimTMaOOqGy9R46psUQEXAMzsTGtZkUz42StHaTt+SkdeZpEKuJyxPBph98EeBoZGwo4iBaCpuZWIGldllQq4nNEQrWFw2Nl9sCfsKJLnuk4GjauuUeOqbFIBlzNGj8hUYyuZrEefa2dAjauyLukCbmalZrbNzDYFl83MPm1mu81sl5n9bvZiylRYOreSyopSTWTKpK1vbmX5oggNdTVhRyloZSmsez+wCxhtJfbrwGJgmbuPmNmCDGeTKVZSYlyxSBOZMjk7Orpoae/mz961POwoBS+pLXAzqwfuAB5IuPq3gT939xEAdz+U+Xgy1Rrqath5oJvhEZ3kWNKzobmNirIS7rpGjauyzZI5G7mZbQQ+A1QDH3P31WZ2FPgC8B7gMPC77r5nnPveB9wHUFtb27hu3bq0gvb29lJVVZXWfbOp0HL9qG2Qf2kZ4DM3zWBRVeanSArt/cq2fMs1MOz83lMnaZhbym+vmJ4zucI22VyrVq3a6u4rz7rB3c/7A6wGvhIs3wJsCpZ7gd8Pln8Z+NFEj9XY2Ojp2rx5c9r3zaZCy9XSfsKXfHyTP7K9PbOBAoX2fmVbvuV6dHu7L/n4Jv/h7kNTGyiQb+9XsoBmH6emJrOJdSNwp5ntB9YBt5rZfwBtwLeCdR4Crkr3t4vkjksXVFNRWsKOdk1kSuqamoPGVRercdVUmLCAu/sn3b3e3ZcC9wJPuvv7gYeBW4PV3gLszlpKmTIVZSVctrBKE5mSsrbjJ/nx3iPc3VhPiRpXTYnJDHJ+Fnivmb1AfHz8Q5mJJGGLLaphR0fX6BCaSFI2bm0DYM1KNa6aKikVcHd/yt1XB8sn3P0Od7/S3W9w9+eyE1GmWqwuwvGTgxzo6g87iuSJkRFnQ3MbN148j/rZalw1VXQkppxl9CTHLRoHlyT99OWjtJ84xdrrdOTlVFIBl7NcsagaM52lXpLX1NxKzYxy3r68NuwoRUUFXM4ys6KMi+ZVqoBLUrpODvL4jk7uWhFV46oppgIu44pFa9ipniiShEeCxlVr1LhqyqmAy7ga6iJ0dPVzrG8g7CiS49ZvaSUWVeOqMKiAy7hGJzLVmVDOp6W9ix0d3WobGxIVcBnXaG9wjYPL+Wxobo03rlqhxlVhUAGXcc2aWUHdrBnalVDOqX9wmIe3d/CO2EJqZpaHHacoqYDLOcWiEZ2dR87pezsP0nVqUMMnIVIBl3OKRWvYd7SPvtNDYUeRHNS0Jd646k0Xzw07StFSAZdzikUjuMOuA9oKl9c7fHKEn7x8hDUr1bgqTCrgck6ju4VpHFzG+nF7/K+yuxvVuCpMKuByTrWRacytrNCeKPI6IyPOj9uHuOkSNa4Kmwq4nJOZsTyqkxzL6/3k5SMc7XdNXuYAFXA5r1i0hj2Hejg9NBx2FMkRTc1tVJbD29S4KnQq4HJeDXURBoedPQd7w44iOeDEyQG+u6OTGxaVqXFVDlABl/PSIfWS6JHtHQwMjfDm+rKwowgq4DKBJXNmUjWtTOPgAsQbVzXURVgS0dZ3LlABl/MqKTGuWFStXQmFlvYudh7o5h5NXuYMFXCZUCxaw64DPQyP6CTHxawpaFx159VqXJUrVMBlQrFohFODw+w70hd2FAlJ/+AwD29r550NalyVS1TAZUKayJTv7uiku39I+37nGBVwmdCltVVUlJZoIrOINTW3Uj97BjdcpMZVuUQFXCZUXlrC5QurtQVepFqPneQne4+ypnGxGlflGBVwSUosOKTeXROZxWbD1jbM4O6ValyVa1TAJSmxaIQTJwfp6OoPO4pMoeERZ2NzKzddMo+6WTPCjiNjqIBLUmJqLVuUfrL3CB1d/dxznSYvc5EKuCTlioURSkwnOS42Tc2tzJpZrsZVOUoFXJIyo6KUi+ZXsVMTmUXjeN8A39txkLtW1DGtTIfO5yIVcElaLBqhpV1b4MXi4e3tDAyPaN/vHKYCLklriNbQ2d3P0d7TYUeRLHN31m9p5cq6GpZHI2HHkXNIuoCbWamZbTOzTWOu/7KZqVl0EYgF/5E1Dl74Wtq7ebGzh7WavMxpqWyB3w/sSrzCzFYCszKaSHLWchXwotHU3Mq0shLuvDoadhQ5j6QKuJnVA3cADyRcVwr8FfCH2YkmuWbWzArqZs2gRROZBa1/cJiHtweNq2aocVUus2SOrDOzjcBngGrgY+6+2szuB0rc/Ytm1uvuVee4733AfQC1tbWN69atSytob28vVVXjPkWoii3Xl7f109YzwuduTu9s5MX2fk1WGLl+1jHE154/zR9eN53lc8ff+0TvV2omm2vVqlVb3X3lWTe4+3l/gNXAV4LlW4BNQBT4MVAWXN870eO4O42NjZ6uzZs3p33fbCq2XH/7g92+5OObvKd/MK37F9v7NVlh5HrfP/3Mb/rcEz48PHLOdfR+pWayuYBmH6emJjOEciNwp5ntB9YBtwI7gEuAvcH1M81sb9q/XiRvjE5k7jqgcfBC1HrsJD99WY2r8sWEBdzdP+nu9e6+FLgXeNLdZ7v7QndfGlx/0t0vyXJWyQENOqS+oG1obo03rmpU46p8oP3AJSULqqcxr6pCe6IUoOERZ+PWNt586XyialyVF1Iq4O7+lLuvHuf63Js1kKwwM5ZHa1TAC9CPRxtX6cjLvKEtcElZLBphz8EeTg8Nhx1FMqipuZXZM8u5bfmCsKNIklTAJWUN0RqGRpzdnToAt1Ac7xvg+zsOctc1alyVT1TAJWWvHVKvicxC8dC2eOMq9f3OLyrgkrIL5sykalqZxsELhLvT1NzKVfU1LFuoxlX5RAVcUlZSYixfFNEh9QXihfaueOMqTV7mHRVwSUusLsKLB3oYHtFJjvPdaOOqd6lxVd5RAZe0xKI1nBocZt8RTWTms/7BYR7Z3sHtVy5S46o8pAIuaVFv8MLweEsnPf1DrFmpIy/zkQq4pOWSBVVUlJXokPo8t35LKxfMmcn1F84NO4qkQQVc0lJeWsKyhdXaAs9jvzh6kp+9cpQ1jfVqXJWnVMAlbbFohB0d3aNthyXPbNgaNK7S8EneUgGXtC2P1tB1apC246fCjiIpGm1cdfOl81lUo8ZV+UoFXNLWoInMvPWjPYc50NWvIy/znAq4pG3ZwgglBjt1QE/eaWpuZU5lBbddURt2FJkEFXBJ24yKUi6eX6Ut8DxzrG+A7+88yF0r6qgoUwnIZ/r0ZFJiUR1Sn28e2tbO4LBr+KQAqIDLpDTU1XCw+zRHek+HHUWS4O5saG7l6voaLl9YHXYcmSQVcJmU5ZrIzCvPtwWNq7T1XRBUwGVSYoviJzlWb/D80NTcyvRyNa4qFCrgMik1M8upnz2DHe3aAs91pwaGeXR7B7c3LCIyXY2rCoEKuExaQ7RGW+B54PEdB+g5PcQa9f0uGCrgMmmxaIT9R0/S0z8YdhQ5j/VbWlkydybXXzQn7CiSISrgMmmxuvhE5k5NZOasV4/28fNXjrGmsR4zNa4qFCrgMmmx6OhEpgp4rtrQ3EaJwXsb1biqkKiAy6QtqJ7GvKppKuA56kzjqsvUuKrQqIDLpJlZ0FpWE5m56Id7DtPZ3c89mrwsOCrgkhGxaIQ9h3rpHxwOO4qM0bQl3rjqrWpcVXBUwCUjGupqGB5xdh/sCTuKJDjae5of7DrIe65R46pCpE9UMkInOc5No42r1mr4pCCpgEtGLJ49k+ppZRoHzyHuTlNzK1cvnqXGVQUq6QJuZqVmts3MNgWXv25mL5lZi5n9q5np2NwiVlJiXBGN0KJD6nPGc21d7D7Yq8nLApbKFvj9wK6Ey18HlgFXAjOAD2Uwl+ShhmgNL3Z2MzyikxzngtHGVauvXhR2FMmSpAq4mdUDdwAPjF7n7o95AHgG0BECRS4WjdA/OMIrh3vDjlL0Tg0M8+3tHdx+pRpXFTKL198JVjLbCHwGqAY+5u6rE24rB54G7nf3H41z3/uA+wBqa2sb161bl1bQ3t5eqqqq0rpvNinXa1p7Rvjjn5zivqum8aZoWc7kSkah5fpJ+yD//MIAn3jDdJbNKc2ZXNlWqLlWrVq11d1XnnWDu5/3B1gNfCVYvgXYNOb2fwa+NNHjuDuNjY2ers2bN6d932xSrtcMDA37pX/0mP/Ft3eccx29X6lJN9far/7U3/L5J31kZCSzgQKF9n5l22RzAc0+Tk1NZgjlRuBOM9sPrANuNbP/ADCzPwXmAx9N+1eLFIzy0hKuWFitXQlDtv9IH0/vO8aalYvVuKrATVjA3f2T7l7v7kuBe4En3f39ZvYh4JeA97n7SJZzSp5YHvQG9ySG5iQ7NmxtjTeuulbTUoVuMvuBfxWoBX5mZtvN7E8ylEnyWCwaobt/iLbjp8KOUpSGhkfYuLWNWy5fwMKa6WHHkSwbf6bpHNz9KeCpYDml+0pxaKh77RyZi+fMDDlN8fnRniMc7D7Np+7U1ncx0JGYklHLFlZTWmIaBw/J+i2tzK2s4NZlalxVDFTAJaOml5dy8fxKFfAQqHFV8dGnLBkXi9bQ0q6eKFPtoW3tDI04a6/TofPFQgVcMi4WjXCo5zSHe06HHaVouDvrt7SyYvEsLqtV46pioQIuGffaOTK1FT5VtreeYM+hXu7R1ndRUQGXjFuu3uBTrqm5jRnlpay+So2riokKuGRczYxyFs+ZoS3wKXJyYIhvPxdvXFWtxlVFRQVcsqIhWqMt8CnynRc66T09xNqV2ve72KiAS1bEohFePXqS7v7BsKMUvPXNrSydO5M3XDgn7CgyxVTAJStGJzJ3ais8q/Yd6eMZNa4qWirgkhU6yfHU2NAcb1x1d6OGT4qRCrhkxYLIdOZXT9NEZhaNNq5adfkCaiNqXFWMVMAla2LRiIZQsuiHew5zqOc0a3TS4qKlAi5ZE4tG2HOol/7B4bCjFKT1W1qZV1XBW69YEHYUCYkKuGRNQ7SG4RHnpc6esKMUnCO9p3li1yHec00d5aX6b1ys9MlL1rx2SL2GUTLtoWeDxlUaPilqKuCSNYvnzKB6ehktmsjMKHenqbmVay6YxaVqXFXUVMAla8yM5Ysi2gLPsG2jjau09V30VMAlqxrqanjxQDdDwzrvdaZsaG5lRnkpd6hxVdFTAZesikUjnB4a4ZUjfWFHKQjxxlUHuOMqNa4SFXDJstGJTJ2hJzMeO9O4SsMnogIuWXbx/EqmlZVoHDxDmra0ctG8Sq5bOjvsKJIDVMAlq8pKS1i2KKJD6jPglcO9PLNfjavkNSrgknWxaHxPFHcPO0pe27C1jdIS473X1oUdRXKECrhkXSwaoad/iNZjp8KOkreGhkf41tY2Vl0+nwVqXCUBFXDJOp3kePL+e7caV8nZVMAl65YtrKa0xDSROQmjjatuXabGVfIaFXDJuunlpVwyv7jrT0wAAAcrSURBVEqH1KfpcM9pnnzxEL98bb0aV8nr6NsgU2J0IlNS99C2tqBxlc66I6+nAi5TIlZXw+Ge05w4rUPqUxFvXNXGtRfM4pIFalwlr6cCLlNi9ByZv+hWAU/FyydG2Huol3uu0+SlnC3pAm5mpWa2zcw2BZcvNLOnzWyPma03s4rsxZR8tzwo4PtVwFPyw/YhZlaUcsdV0bCjSA5KZQv8fmBXwuXPAV9090uB48AHMxlMCktkejkXzJmpLfAU9J0e4pkDQ9xx5SKqppWFHUdyUFLfCjOrB+4APg181OLH8d4K/I9glQeBPwP+MQsZpUA01EX4/o5O3vaF/w47yln6Tp6k8tncynVqcJj+YVir4RM5B0vm8GYz2wh8BqgGPgb8OvBzd78kuH0x8B13bxjnvvcB9wHU1tY2rlu3Lq2gvb29VFVVpXXfbFKu5L10bJjHXzlFaWnubU0ODw1RWpZ7uWrKhnh/Q2XO9T7Jxe8XFG6uVatWbXX3lWfd4O7n/QFWA18Jlm8BNgHzgb0J6ywGXpjosRobGz1dmzdvTvu+2aRcqVGu1ChXago1F9Ds49TUZDY5bgTuNLPbgelABPgSMMvMytx9CKgHOtL+9SIiIimbcBLT3T/p7vXuvhS4F3jS3X8F2AzcHaz2AeCRrKUUEZGzTGY/8I8Tn9DcC8wF/iUzkUREJBkpzdq4+1PAU8HyK8AbMh9JRESSoSMxRUTylAq4iEieUgEXEclTKuAiInkqqSMxM/ZkZoeBV9O8+zzgSAbjZIpypUa5UqNcqSnUXEvcff7YK6e0gE+GmTX7eIeShky5UqNcqVGu1BRbLg2hiIjkKRVwEZE8lU8F/J/CDnAOypUa5UqNcqWmqHLlzRi4iIi8Xj5tgYuISAIVcBGRPJUXBdzM3mFmL5nZXjP7xBQ/97+a2SEza0m4bo6ZfT84ofP3zWx2cL2Z2d8FOZ83s2uzlGmxmW02s11mtsPM7s+FXMFzTTezZ8zsuSDbp4Lrxz0JtplNCy7vDW5fmsVsSZ2YeyozBc+338xeMLPtZtYcXJcLn+UsM9toZi8G37Ubws5lZpcH79PoT7eZfSTsXMFz/V7wnW8xs28G/xey+x0b7ywPufQDlAIvAxcBFcBzwPIpfP6bgWuBloTrPg98Ilj+BPC5YPl24DuAAdcDT2cp0yLg2mC5GtgNLA87V/BcBlQFy+XA08FzNgH3Btd/FfjtYPl/Al8Nlu8F1mcx20eBbwCbgsuhZwqeYz8wb8x1ufBZPgh8KFiuAGblQq6EfKVAJ7Ak7FxAHbAPmJHw3fr1bH/HsvoGZ+iNuQH4bsLlTwKfnOIMS3l9AX8JWBQsLwJeCpa/BrxvvPWynO8R4G05mGsm8CzwRuJHoZWN/UyB7wI3BMtlwXqWhSz1wBPET8a9KfgPHWqmhGz7ObuAh/pZEj/z1r6xrzvsXGOyvB34SS7kIl7AW4E5wXdmE/BL2f6O5cMQyugbM6otuC5Mte5+ACD4d0Fw/ZRnDf70uob4lm5O5AqGKrYDh4DvE/8L6oTHT7839vnPZAtu7yJ+gpBM+xLwh8BIcHluDmQa5cD3zGyrxU8CDuF/lhcBh4H/Gww7PWBmlTmQK9G9wDeD5VBzuXs78NfAL4ADxL8zW8nydywfCvh4p+PO1X0fpzSrmVUB3wI+4u7d51t1nOuylsvdh919BfGt3jcAV5zn+bOezcxWA4fcfWvi1WFmGuNGd78WeCfwv8zs5vOsO1XZyogPHf6ju18D9BEfmgg7V/zJ4mPJdwIbJlp1nOsynisYc383cCEQBSqJf57neu6M5MqHAt5G/Kz3o3LhBMoHzWwRQPDvoeD6KctqZuXEi/fX3f0/cyVXInc/QfwMTtcTnAR7nOc/ky24vQY4luEooyfm3g+sIz6McubE3CFlOsPdO4J/DwEPEf+lF/Zn2Qa0ufvTweWNxAt62LlGvRN41t0PBpfDznUbsM/dD7v7IPCfwJvI8ncsHwr4FuDSYDa3gvifTY+GnOlR4idyhtef0PlR4NeCme/rga7RP+syycyM+DlId7n7F3IlV5BtvpnNCpZnEP9i7+LcJ8FOzHw38ZNmZ3QLyVM/MXfWM40ys0ozqx5dJj6u20LIn6W7dwKtZnZ5cNVbgZ1h50rwPl4bPhl9/jBz/QK43sxmBv8/R9+v7H7HsjnJkMEJgtuJ72nxMvBHU/zc3yQ+pjVI/LfmB4mPVT0B7An+nROsa8A/BDlfAFZmKdNNxP/ceh7YHvzcHnau4LmuArYF2VqAPwmuvwh4BthL/M/eacH104PLe4PbL8ry53kLr+2FEnqmIMNzwc+O0e93jnyWK4Dm4LN8GJidI7lmAkeBmoTrciHXp4AXg+/9/wOmZfs7pkPpRUTyVD4MoYiIyDhUwEVE8pQKuIhInlIBFxHJUyrgIiJ5SgVcRCRPqYCLiOSp/w9BG6kfdrpRHAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import control\n", "import pandas as pd\n", "\n", "# Relay Control\n", "def relay(Qmin, Qmax):\n", " Q = 0\n", " while True:\n", " Tsp, T = yield Q\n", " Q = Qmax if T < Tsp else Qmin\n", " \n", "# Temperature Setpoint\n", "def Tsp(t):\n", " tp = [ 0, 200, 300, 500, 700, 9999]\n", " Tp = [ 50, 50, 40, 40, 50, 50]\n", " return np.interp(t, tp, Tp)\n", "\n", "t = np.linspace(0, 800, 801)\n", "plt.plot(t, Tsp(t))\n", "plt.title('Temperature Setpoint')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 1. Open-Loop State Estimation and Relay Control\n", "\n", "In this first experiment, use a closed loop estimator and relay control to cause the heater to track a desired temperature setpoint. Perform the following tasks:\n", "\n", "**a.**) This code was cut and paste from Thursday's lecture. Adjust the setup to interface with the lab device rather than simuation. Modify the parameters to reflect the model you previously fit to the TCLab hardware.\n", "\n", "**b.**) When you run the experiment, keep the Arduino/TCLab device carefully shielded from any air currents. For example, you might use the plastic lab kit as a shield.\n", "\n", "**c.**) Describe what you see. Does the controller track the setpoint? Does the controller reject disturbances? Explain why this system displays the behavior you're seeing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Open Loop Estimation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import control \n", "\n", "# This function runs a model of the temperature control lab in parallel\n", "# with real-time control. This uses the Python 'yield' statement o \n", "# share data with a calling script.\n", "\n", "def tclab():\n", " # parameter estimates. Modify these to reflect your model fit.\n", " 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 # ambient room temperature\n", "\n", " # state space model\n", " A = [[-(Ua + Uc)/CpH, Uc/CpH], [Uc/CpS, -Uc/CpS]]\n", " B = [[P/CpH], [0]] # single column\n", " C = [[0, 1]] # single row\n", " D = [[0]]\n", "\n", " # initialize variables that will be updated in time\n", " t = 0\n", " x = np.array([0, 0])\n", " \n", " while True:\n", " # yield the heater temperature to the calling program\n", " Th = x[0] + Tamb\n", " Ts = x[1] + Tamb\n", " tnext, Q, T1 = yield Th, Ts\n", " dt = tnext - t\n", " x = x + dt*np.dot(A, x) + dt*np.dot(B, [Q])\n", " t = tnext" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Event Loop" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "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 = relay(0, 80)\n", "controller.send(None)\n", "\n", "# create an model estimator\n", "estimator = tclab()\n", "estimator.send(None)\n", "\n", "# execute the event loop\n", "tf = 800\n", "with TCLab() as lab:\n", " h = Historian([('SP', lambda: Tsp(t)), ('T1', lambda: lab.T1), ('Q1', lab.Q1),\n", " ('Th', lambda: Th), ('Ts', lambda: Ts), ('T1-Ts', lambda: T1-Ts)])\n", " p = Plotter(h, tf)\n", " U1 = 0\n", " for t in clock(tf, 2): # allow time for more calculations\n", " T1 = lab.T1 # measure the sensor temperature\n", " Th, Ts = estimator.send([t, U1, T1]) # estimate the heater temperature\n", " SP = Tsp(t) # get setpoint\n", " U1 = controller.send([SP, Th]) # compute control action\n", " lab.U1 = U1 # set manipulated variable \n", " p.update(t) # log data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 2. Closed-Loop State Estimation and Relay Control\n", "\n", "In the second experiment, use a closed loop estimator and relay control to cause the heater to track a desired temperature setpoint. \n", "\n", "Before performing the experiment, you will need to determine suitable values for the observer gain $L$. $L$ is a matrix consisting of two rows and one column. You need to choose the values in $L$ such that $A-LC$ is stable and has acceptable time constants. For this experiment we will use a pole placement technique to determine $L$.\n", "\n", "\\begin{align*}\n", "L & = \\begin{bmatrix} ? \\\\ ? \\end{bmatrix}\n", "\\end{align*}\n", "\n", "**a.**) Before starting the experiment, compute the eigenvalues and corresponding time constants of $A$ and of $A-LC$. \n", "\n", "**b.**) Then repeat the same tasks for Experiment 1, but this time use a closed-loop estimator. In your answers, be sure to describe how the results of this experiment differ from the first, and explain why." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Review of Closed Loop Estimation\n", "\n", "Examining the results, we see the predicted heater temperature appears to track the desired setpoint, but the sensor temperature is off the mark. This could be due several factors, most likely model error.\n", "\n", "Process\n", "\\begin{align*}\n", "\\frac{dx}{dt} & = A x + B u \\\\\n", "y & = C x + D u\n", "\\end{align*}\n", "\n", "Model\n", "\\begin{align*}\n", "\\frac{d\\hat{x}}{dt} & = A \\hat{x} + B u \\\\\n", "\\hat{y} & = C \\hat{x} + D u\n", "\\end{align*}\n", "\n", "If the measured and estimated outputs are different, then $y - \\hat{y} \\neq 0$. Taking the difference we find\n", "\n", "\\begin{align*}\n", "y - \\hat{y} & = C x + Du - C\\hat{x} - Du \\\\\n", "& = C(x - \\hat{x})\n", "\\end{align*}\n", "\n", "The difference $y - \\hat{y}$ can be observed. But what we want is to make $x - \\hat{x}$ small. Is there way to use knowledge of $y - \\hat{y}$ to reduce $x - \\hat{x}$?\n", "\n", "The technique is to introduce feedback $L(y-\\hat{y})$ to the model. \n", "\n", "\\begin{align*}\n", "\\frac{d\\hat{x}}{dt} & = A \\hat{x} + B u + L(y - \\hat{y}) \\\\\n", "\\hat{y} & = C \\hat{x} + D u\n", "\\end{align*}\n", "\n", "Subtracting the new model equations from the process model, we get an expression for the dynamics of the model error $e = x - \\hat{x]$\n", "\n", "\\begin{align*}\n", "\\frac{de}{dt} & = \\frac{dx}{dt} - \\frac{d\\hat{x}}{dt} \\\\\n", "\\\\\n", "& = (Ax + Bu) - (A\\hat{x} + Bu + L(Cx - C\\hat{x})) \\\\\n", "\\\\\n", "& = (A - LC)(x -\\hat{x}) \\\\\n", "\\\\\n", "\\frac{de}{dt} & = (A - LC) e\n", "\\end{align*}\n", "\n", "The choice of $L$ determines observer performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choosing $L$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time constants of A are: [121.33951135 27.35329911] seconds\n" ] } ], "source": [ "import numpy as np\n", "from scipy.signal import place_poles\n", "\n", "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 # ambient room temperature\n", "\n", "# state space model\n", "A = np.array([[-(Ua + Uc)/CpH, Uc/CpH], [Uc/CpS, -Uc/CpS]])\n", "C = np.array([[0, 1]]) # single row\n", "\n", "# be sure all eigenvalues have negative real parts\n", "assert(all(np.real(eigenvalues) < 0))\n", "\n", "# get the eigenvalues of A and report time constants\n", "eigenvalues, _ = np.linalg.eig(A)\n", "print('Time constants of A are:', -1.0/np.real(eigenvalues), 'seconds')\n", "\n", "# place poles of observer to be at least three time faster than open loop estimator\n", "# L = place_poles(A.T, C.T, 3*eigenvalues).gain_matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next cell, choose values for L that cause both time constants of the observer to be smaller than the times constants of A, but keep the component values of L small. This will require some trial and error." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time constants of A - LC are: [22.24490704 0.50003853] seconds\n" ] } ], "source": [ "L = np.array([[2],[2]])\n", "\n", "eigenvalues_observer, _ = np.linalg.eig(A - np.dot(L, C))\n", "print('Time constants of A - LC are:', -1.0/np.real(eigenvalues_observer), 'seconds')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import control \n", "\n", "# The following function implements a state observer\n", "\n", "def tclab():\n", " # parameter estimates\n", " 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 # ambient room temperature\n", "\n", " # state space model\n", " A = [[-(Ua + Uc)/CpH, Uc/CpH], [Uc/CpS, -Uc/CpS]]\n", " B = [[P/CpH], [0]] # single column\n", " C = [[0, 1]] # single row\n", " D = [[0]]\n", "\n", " # initialize variables that will be updated in time\n", " t = 0\n", " x = np.array([0, 0])\n", " \n", " # observer gain ... put your values here!\n", " L = [[0.6], [0.3]]\n", " \n", " while True:\n", " # yield the heater temperature to the calling program\n", " Th = x[0] + Tamb\n", " Ts = x[1] + Tamb\n", " tnext, Q, T1 = yield Th, Ts\n", " dt = tnext - t\n", " x = x + dt*np.dot(A, x) + dt*np.dot(B, [Q]) + dt*np.dot(L, [T1-Ts])\n", " t = tnext" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "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 = relay(0, 80)\n", "controller.send(None)\n", "\n", "# create an model estimator\n", "estimator = tclab()\n", "estimator.send(None)\n", "\n", "# execute the event loop\n", "tf = 800\n", "with TCLab() as lab:\n", " h = Historian([('SP', lambda: Tsp(t)), ('T1', lambda: lab.T1), ('Q1', lab.Q1),\n", " ('Th', lambda: Th), ('Ts', lambda: Ts), ('T1-Ts', lambda: T1-Ts)])\n", " p = Plotter(h, tf)\n", " U1 = 0\n", " for t in clock(tf, 2): # allow time for more calculations\n", " T1 = lab.T1 # measure the sensor temperature\n", " Th, Ts = estimator.send([t, U1, T1]) # estimate the heater temperature\n", " SP = Tsp(t) # get setpoint\n", " U1 = controller.send([SP, Th]) # compute control action\n", " lab.U1 = U1 # set manipulated variable \n", " p.update(t) # log data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question\n", "\n", "Be sure to review the code given above to understand how this code is working. Then, in the cells below, implement a proportional controller with a proportional control gain Kp = 0.8. Test the controller in simulation mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [State Estimation](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/03.00-State-Estimation.ipynb) | [Contents](toc.ipynb) | [Feedback Control](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/04.00-Feedback-Control.ipynb) >

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\"Download\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [State Estimation](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/03.00-State-Estimation.ipynb) | [Contents](toc.ipynb) | [Feedback Control](http://nbviewer.jupyter.org/github/jckantor/CBE32338/blob/master/notebooks/04.00-Feedback-Control.ipynb) >

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