control-system/Assignment 6 - Tuning PID/assign_6_pso.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Tugas kelompok sisken</h1>\n",
        "<p>\n",
        "Anggota:\n",
        "Nanda Riangga Damanik (19/442385/PA/19134)<br>\n",
        "Gabriel Possenti (19/442374/PA/19123)<br>\n",
        "Aziz<br>\n",
        "Ivander Achmad W. (19/442377/PA/19126)<br>\n",
        "\n",
        "</p>"
      ],
      "metadata": {
        "id": "NVGE7Ov689Yv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install slycot\n",
        "!pip install control"
      ],
      "metadata": {
        "id": "rh1dEWqiX2eC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b54a86a3-fa44-408d-b9ca-ef57768a8b45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting slycot\n",
            "  Downloading slycot-0.5.0.0.tar.gz (3.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0 MB 9.0 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from slycot) (1.21.6)\n",
            "Building wheels for collected packages: slycot\n",
            "  Building wheel for slycot (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for slycot: filename=slycot-0.5.0-cp37-cp37m-linux_x86_64.whl size=2021296 sha256=6b9d8a0f20baf1ad97ce3f430e682ef65ced6852f40883eea452ecae431e988f\n",
            "  Stored in directory: /root/.cache/pip/wheels/7f/2e/54/e2cb7cf358f956ed0efa9923ab4f5aa353a941422c55b25531\n",
            "Successfully built slycot\n",
            "Installing collected packages: slycot\n",
            "Successfully installed slycot-0.5.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting control\n",
            "  Downloading control-0.9.2.tar.gz (398 kB)\n",
            "\u001b[K     |████████████████████████████████| 398 kB 7.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from control) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from control) (1.7.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from control) (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->control) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->control) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->control) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->control) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->control) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->control) (1.15.0)\n",
            "Building wheels for collected packages: control\n",
            "  Building wheel for control (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for control: filename=control-0.9.2-py2.py3-none-any.whl size=403205 sha256=f381cd2cc15a36939fc5efc273768704c4457221132d0aac23a24b2d42574943\n",
            "  Stored in directory: /root/.cache/pip/wheels/48/ef/c2/929bb5c59a1328df00a0561d0e68cd7c8537f33f5ce0ce741b\n",
            "Successfully built control\n",
            "Installing collected packages: control\n",
            "Successfully installed control-0.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlHXy4vZR402",
        "outputId": "e79671b0-0c87-4431-8125-6dfbb452a639"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5555.291048537736\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import control.matlab as control\n",
        "\n",
        "def step_info(t,yout):\n",
        "    print(\"% overshoot : \",(yout.max()/yout[-1]-1)*100,'%')\n",
        "    print(\"Rise time : \",t[next(i for i in range(0,len(yout)-1) if yout[i]>yout[-1]*.90)]-t[0])\n",
        "    print(\"%Settling time : \",t[next(len(yout)-i for i in range(2,len(yout)-1) if abs(yout[-i]/yout[-1])>1.02)]-t[0])\n",
        "\n",
        "s = control.tf('s')\n",
        "J = 0.01\n",
        "b = 0.1\n",
        "K = 0.01\n",
        "R = 1\n",
        "L = 0.5\n",
        "\n",
        "num_motor = [K]\n",
        "den_motor = [J*L, J*R+b*L, R*b+K*K]\n",
        "\n",
        "motor = control.tf(num_motor, den_motor)\n",
        "\n",
        "# Constant\n",
        "c1=2\n",
        "c2=2 \n",
        "w_max = 1 \n",
        "w_min = 0.1 \n",
        "particles=50 \n",
        "iterations=100\n",
        "var=3 \n",
        "e_max = 1 \n",
        "e_min=0.1\n",
        "\n",
        "# Declaration\n",
        "v = np.zeros([particles, var])\n",
        "x = np.zeros([particles, var])\n",
        "xp = np.zeros([particles, var])\n",
        "xg = np.zeros([var])\n",
        "ITAEp = np.zeros([particles])\n",
        "ITAE = np.zeros([particles])\n",
        "best_value = np.zeros([999999])\n",
        "\n",
        "\n",
        "# Search limit\n",
        "lim_min = 0\n",
        "lim_max = 2500\n",
        "\n",
        "# limization steps\n",
        "steps = 0\n",
        "\n",
        "# Initialization\n",
        "for m in range(particles):\n",
        "    for n in range(var):\n",
        "        v[m,n]=0\n",
        "        x[m,n]=lim_min+np.random.rand()*(lim_max-lim_min)\n",
        "        xp[m,n]=x[m,n]\n",
        "\n",
        "    # Model Parameters\n",
        "    Kp = x[m,0]\n",
        "    Ki = x[m,1]\n",
        "    Kd = x[m,2]\n",
        "    # Simulation Model\n",
        "    pid = control.tf([Kd,Kp,Ki],[0,1,0])\n",
        "    motor_cl = control.feedback(motor * pid, 1)\n",
        "    [y,t] = control.step(motor_cl)\n",
        "    # TIAE (Objective Function)\n",
        "    total = 0.0\n",
        "    T = len(y)\n",
        "    for t in range(T):\n",
        "        total = total + t*abs(y[t]-1)\n",
        "    ITAE[m] = total\n",
        "\n",
        "print(ITAE[0])\n",
        "print(type(ITAE))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prev_best = min(ITAE)\n",
        "loc = np.searchsorted(ITAE, min(ITAE))\n",
        "xg[0] = x[loc,0]\n",
        "xg[1] = x[loc,1]\n",
        "xg[2] = x[loc,2]"
      ],
      "metadata": {
        "id": "xY1bhLrhz5tu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(iterations):\n",
        "  e = e_max - ((e_max - e_min)*i)/iterations\n",
        "  w = w_min + ((iterations - i)*(w_max - w_min))/iterations\n",
        "  for m in range(particles):\n",
        "    for n in range(var):\n",
        "      v[m,n] = w*v[m,n] + c1*np.random.rand()*(xp[m,n]-x[m,n]) + c2*np.random.rand()*(xg[n]-x[m,n])\n",
        "      x[m,n] = x[m,n] + e*v[m,n];\n",
        "      # Constrain\n",
        "      if x[m,n] < lim_min:\n",
        "        x[m,n] = lim_min\n",
        "      if x[m,n] > lim_max:\n",
        "        x[m,n] = lim_max\n",
        "    \n",
        "    Kp = x[m,0]\n",
        "    Kp = x[m,1]\n",
        "    Kp = x[m,2]\n",
        "    pid = control.tf([Kd, Kp, Ki],[0,1,0])\n",
        "    motor_cl = control.feedback(motor * pid, 1)\n",
        "    [y,t] = control.step(motor_cl)\n",
        "\n",
        "    total = 0\n",
        "    T = len(y)\n",
        "    for t in range(T):\n",
        "      total = total + (t*abs(y[t]-1))\n",
        "    \n",
        "    ITAEp[m] = total\n",
        "\n",
        "    if ITAEp[m] < ITAE[m]:\n",
        "      ITAE[m] = ITAEp[m]\n",
        "      xp[m,0] = x[m,0]\n",
        "      xp[m,1] = x[m,1]\n",
        "      xp[m,2] = x[m,2]\n",
        "    \n",
        "\n",
        "    now_best = min(ITAE)\n",
        "    loc = np.searchsorted(ITAE, min(ITAE))\n",
        "\n",
        "    if now_best < prev_best:\n",
        "      prev_best = now_best\n",
        "      xg[0] = x[loc,0]\n",
        "      xg[1] = x[loc,1]\n",
        "      xg[2] = x[loc,2]\n",
        "    \n",
        "    steps = steps + 1\n",
        "    best_value[steps] = prev_best"
      ],
      "metadata": {
        "id": "0fC4tNGBxRx9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Final testing\n",
        "ITAE_min = prev_best\n",
        "Kp = xg[0]\n",
        "Ki = xg[1]\n",
        "Kd = xg[2]\n",
        "\n",
        "pid = control.tf([Kd, Kp, Ki],[0,1,0])\n"
      ],
      "metadata": {
        "id": "4EysKchgx--6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "motor_cl = control.feedback(motor * pid, 1)\n",
        "motor_l = control.feedback(motor, 1)\n",
        "\n"
      ],
      "metadata": {
        "id": "WZ1DM3Y7i2m0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(t,y) = control.step(motor_l)\n",
        "\n",
        "plt.plot(t,y)\n",
        "plt.xlabel('motor_l')\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "DTiYuZi00l7C",
        "outputId": "301f6874-e439-4908-89c3-6e6a4b27b655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(t,y) = control.step(motor_cl)\n",
        "\n",
        "plt.plot(t,y)\n",
        "plt.xlabel('motor_cl')\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "dxMJTkSX2DsB",
        "outputId": "d3298363-ae4a-479c-d3a5-2e89e622d219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(t,y) = control.step(motor_l/(s**2))\n",
        "\n",
        "plt.plot(t,y)\n",
        "plt.xlabel('motor_l')\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "nEa8Y4Xy2PZu",
        "outputId": "328581ff-0c7d-45ae-8333-380e4e5ac045"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEHCAYAAAC3Ph1GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8dc3G0vCFpYkhCXssgSRRFBQm6hUwRWtWmrrghXr0tpqe2vVtt7eX6v19rZ6e9u61AUsQlFBURGLSEDcMAlLAhGBECA7YclCQpaZ7++PDDYiSwiTnDkz7+fjkUdmzsw5+Xxz5O3Jd77n+zXWWkRExH3CnC5ARETaRgEuIuJSCnAREZdSgIuIuJQCXETEpSI68of16dPHJiUltWnfQ4cOER0d7d+CAoja525qn7sFevuysrIqrLV9j97eoQGelJREZmZmm/bNyMggLS3NvwUFELXP3dQ+dwv09hljdh1ru7pQRERcSgEuIuJSCnAREZdSgIuIuJQCXETEpU4a4MaYgcaYVcaYLcaYzcaYe33bHzHGFBljNvi+ZrR/uSIickRrhhE2Afdba7ONMd2ALGPMCt9rf7LW/qH9yhMRkeM56RW4tbbEWpvte1wN5AGJ7V2YiEgw2LO/lr9/kM/B2ga/H9ucynzgxpgkYA0wDrgPuAWoAjJpvko/cIx95gBzAOLi4lIWLlzYpkJramqIiYlp075uoPa5m9rnbv5sn7WW4kOWrLImsso87KryAnDPhE6kxrft3sn09PQsa23q0dtbHeDGmBhgNfBba+1iY0wcUAFY4L+ABGvt7BMdIzU11epOzGNT+9xN7XO3022ftZacokqW55ayfHMp+XsPATBxUE8uHRfPJWPjGdy77bfqG2OOGeCt+t+BMSYSeA2Yb61d7Cu4rMXrzwJvtbk6ERGX8XgtWbsOsDy3lHc3l1J0sI7wMMO5Q3tz69QhfHNMHHHdO7drDScNcGOMAZ4D8qy1f2yxPcFaW+J7OhPIbZ8SRUQCQ6PHyyf5+3gnt5R/bS6joqaeqIgwLhjRh59MG8nFo/vRs2tUh9XTmivwqcD3gBxjzAbftgeBWcaYCTR3oRQAd7RLhSIiDqpv8vDR9n0syylhRV4ZB2sb6RoVTvqoflw6Lp70M/oR06lD5wX80kl/qrV2LWCO8dIy/5cjIuK8w40e1nyxl3dyS3lvSxnV9U106xTBxWPiuHRcPN8Y2ZfOkeFOl9mx08mKiASqugYPq78o5+2cUt7PK+NQg4eeXSO5dFw8M5ITmDK8N50inA/tlhTgIhKyahuaWPX5XuZuOMxd76+gtsFDbHQUV07oz4zkBM4Z2pvI8MCdcUQBLiIh5UhoL8sp4f3Py6lr9NA9CmaeNYjLkhOYNCSWiAAO7ZYU4CIS9OoaPKzaWs7bm/4d2n1iorg2JZEZyQkc3p3DhenJTpd5yhTgIhKUDjd6yNhazlubSliZ9/XQnjykN+FhzeMzMvYca5xG4FOAi0jQODJ6pDm0mz+I7B0dxTUTE7ls/FdDOxgowEXE1Ro9XtZuq+DNTcWs2Nw85K9X10iunNCfy8f3Z7KL+rRPlQJcRFzH47V8kr+PNzcWs3xzKQdrG+nWOYJLx8Vz+Zn9mTIssEeP+IsCXERcweu1ZO8+wJsbi3k7p5SKmnqio8KZNiaOy8f35/yRfQJunHZ7U4CLSMCy1rK5uIo3Nxbz1qYSig7W0SkijItG9+OK8f1JP6NfQNwR6RQFuIgEnPy9NSzdWMzSDcXkVxwiIsxwwci+/PSSkUwbE+/Y3COBRr8FEQkIJZV1vLWxhDc2FpFbVIUxMHlILN8/fyjTx8XTK7rjZvlzCwW4iDjmYG0D7+SW8saGIj7duR9r4cwBPXj4stFcPr4/8T3adz5tt1OAi0iHqmvw8F5eGW9sKGb1F+U0eixD+0bz44tGcuWE/gzp0/aVa0KNAlxE2p3Ha/loRwVL1hfxbm4phxo8xHXvxC1TkrhqQiJj+3enee0YORUKcBFpF9ZacouqWLK+iDc3FbO3up5unSK4bHwCV09IZPLQ4Lor0gkKcBHxq8IDtbyxoZgl64vYXl5DVHgYaaP6cvVZiVwY4sP+/E0BLiKnrbKukWU5JSxZX8S6nfsBmJQUy+9mJjMjOb5D14kMJQpwEWmTRo+X1Vv3smR9ESvyymho8jK0bzQ//eZIrpqQyMDYrk6XGPQU4CLSatZacooqWZxdxNKNxew/1EBsdBTfmTSImWclMn5AD30Y2YEU4CJyUgcOe/lbxg4WZxeyrbyGqIgwpo2OY+ZZiXxjVN+QmDgqECnAReSY6ho8vLu5lNeyC1m7rQ7L56QM7sXvZiZzWXICPbpGOl1iyFOAi8iXrLVk7jrAq5mFvJ1TQk19E4k9u3DFsEjumzmVJN1kE1AU4CJC0cE6FmcV8mp2Ibv21dI1KpwZyQlcO3EAk4fEsmbNaoV3AFKAi4SoI10kr2Tt4aMd+7AWzh3amx9dOIJLx8UTrRn/Ap7OkEgIsdayYc9BFmUW8tbGYqrrmxjQqwv3XjSCaycO0NA/l1GAi4SAvdX1vL6+iEWZe9hWXkOXyHCmJ8dzXcpAJg+JJUy3tLuSAlwkSDV5vGRs3cuizD28/3k5TV7LxEE9eeyaZC4bn0C3zhpF4nYKcJEgU1BxiEWZe3g1q5Dy6nr6xERx23lDuC51AMP7dXO6PPEjBbhIEDjc6OGd3BIWrtvDpzv3E2YgfVQ/rj97IBee0U832gQpBbiIi+WVVLFw3W6WrC+i6nATg3t35WeXjOLaiQO0mk0IOGmAG2MGAvOAOMACz1hrnzTGxAL/BJKAAuB6a+2B9itVRABq6pt4c2MxC9ftZmNhJVERYUwfF88NZw/knCG99YFkCGnNFXgTcL+1NtsY0w3IMsasAG4BVlprHzPGPAA8APy8/UoVCW05hZW8vG43SzcUcajBw8i4GH51+RhmnpWoBX9D1EkD3FpbApT4HlcbY/KAROAqIM33trlABgpwEb+qqW/i9fVFLFi3m83FVXSODOOK8f2ZNXkQZw3sqZn/Qpyx1rb+zcYkAWuAccBua21P33YDHDjy/Kh95gBzAOLi4lIWLlzYpkJramqIiYlp075uoPa5m7/bV1DpIWNPEx+XNFHvgYHdwkgbGME5CRFER3Z8aOv8OSs9PT3LWpt69PZWB7gxJgZYDfzWWrvYGHOwZWAbYw5Ya3ud6Bipqak2MzPzFEtvlpGRQVpaWpv2dQO1z9380b7ahua+7fmf7mZTYSWdI8O4fHx/vhMAV9s6f84yxhwzwFs1CsUYEwm8Bsy31i72bS4zxiRYa0uMMQlAuf/KFQkdX5RVM/+TXSzOLqK6vomRcTE8csUYZk4cQI8uutlGjq81o1AM8ByQZ639Y4uXlgI3A4/5vr/RLhWKBKGGJi/LN5fyj092sW7nfqLCw5iRHM+N5wwmdXAv9W1Lq7TmCnwq8D0gxxizwbftQZqDe5Ex5jZgF3B9+5QoEjyKDtax4NPdLPxsNxU1DQyK7covpp/Bt1IG0Dumk9Plicu0ZhTKWuB4lwMX+bcckeDj9VrWbq/gpU92sTKvDIALz4jje+cO5vzhfTRuW9pMd2KKtJPKukZeyyrkpU92sbPiEL2jo7gzbRizJg1iQC9N2yqnTwEu4mefl1Yx7+NdLMkuoq7Rw8RBPbn3hglMT46nU0S40+VJEFGAi/iBx2t5J6eEuR8X8En+fjpFhHHVhP7cdG4S4xJ7OF2eBCkFuMhp2H+ogYWf7ebva+rYfzibxJ5deGD6GdyQOlC3t0u7U4CLtEFeSRUvfljA6xuKqG/yMjo2jEevm8jFo+MI14eS0kEU4CKt5PFa3ssr44UPd/JJ/n46R4ZxbcoAbpmSRHFeFmlj450uUUKMAlzkJKoPN7Ios5AXP9rJnv11JPbswi+mn8ENZw+kZ9fmbpLiPIeLlJCkABc5jt37annho528kllITX0TZyf14sHpo5k2Jo4IrXAjAUABLtKCtZbMXQf4+wf5rNhSRpgxXHFmf26dmsT4A
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(t,y) = control.step(motor_cl/(s**2))\n",
        "\n",
        "plt.plot(t,y)\n",
        "plt.xlabel('motor_cl')\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "qMQ5T9Vo2V4O",
        "outputId": "25a517c8-62fa-4142-f113-8e8074c933c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def step_info(t,yout):\n",
        "    try:\n",
        "      print(\"% overshoot : \",(yout.max()/yout[-1]-1)*100,'%')\n",
        "    except:\n",
        "      print(\"% overshoot : -\")\n",
        "    try:\n",
        "      print(\"Rise time : \",t[next(i for i in range(0,len(yout)-1) if yout[i]>yout[-1]*.90)]-t[0])\n",
        "    except:\n",
        "      print(\"Rise time : -\")\n",
        "    try:\n",
        "      print(\"%Settling time : \",t[next(len(yout)-i for i in range(2,len(yout)-1) if abs(yout[-i]/yout[-1])>1.02)]-t[0])\n",
        "    except:\n",
        "      print(\"%Settling time : -\")\n",
        "\n",
        "y,t = control.step(motor_l)\n",
        "step_info(t,y)\n",
        "\n",
        "print(\"\")\n",
        "\n",
        "y,t = control.step(motor_cl)\n",
        "step_info(t,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv3JS6zCRaf6",
        "outputId": "fb2bc57e-7905-448d-d9be-1a81155a4cee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% overshoot :  0.0 %\n",
            "Rise time :  1.1315389526151183\n",
            "%Settling time : -\n",
            "\n",
            "% overshoot :  0.002374076125843949 %\n",
            "Rise time :  0.002821300652605195\n",
            "%Settling time : -\n"
          ]
        }
      ]
    }
  ]
}