From d68c4a23af0b4ac45a1058dadb4b2c6cd22280a9 Mon Sep 17 00:00:00 2001
From: Gabriel Kheisa <63294471+gabrielkheisa@users.noreply.github.com>
Date: Thu, 29 Sep 2022 12:14:07 +0700
Subject: [PATCH] Add files via upload

---
 Assignment 6 - Tuning PID/assign_6_pso.ipynb | 474 +++++++++++++++++++
 1 file changed, 474 insertions(+)
 create mode 100644 Assignment 6 - Tuning PID/assign_6_pso.ipynb

diff --git a/Assignment 6 - Tuning PID/assign_6_pso.ipynb b/Assignment 6 - Tuning PID/assign_6_pso.ipynb
new file mode 100644
index 0000000..b0d2cf3
--- /dev/null
+++ b/Assignment 6 - Tuning PID/assign_6_pso.ipynb	
@@ -0,0 +1,474 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "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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\n"
+          },
+          "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": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEHCAYAAACHsgxnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATX0lEQVR4nO3de4yldX3H8fd3Z3cVGLyBTgygW+MlIlR0x7u1M6AGqQFbbQNV64V0orUGbU2sNW20mrQ11VYjiWwqQq06KopBvDRW9gRFF9kVFpaLCIi6iC43gWGX2Z053/5xzq4LncszZ84z53fmvF/JhHP2PJfvd87Mh2d+53l+T2QmkqRyrel1AZKkhRnUklQ4g1qSCmdQS1LhDGpJKtzaOjZ65JFH5oYNGzpa94EHHuCwww7rbkGFs+fVb9D6BXteqm3btt2ZmY+f67VagnrDhg1s3bq1o3UbjQZjY2PdLahw9rz6DVq/YM9LFRE/n+81hz4kqXAGtSQVzqCWpMIZ1JJUOINakgpnUEtS4QxqSSrcokEdEc+IiKsO+rovIt61EsVJUr/44c138b2d+2rZ9qJBnZk/ycwTMvMEYCOwG7iwlmokqU9dtP02vvLTHgX1w5wE3JyZ815BI0nqrljKHV4i4lzgx5n5yTlemwAmAEZGRjZOTk52VNDU1BTDw8Mdrduv7Hn1G7R+YfB6/syOaa7ctY9PnNhZz+Pj49syc3Su1yoHdUSsB34FPCszf7PQsqOjo+lcH9XZ8+o3aP3C4PX8vq9ezbe27+SqD57S0foRMW9QL2Xo41W0jqYXDGlJUnctJajPAL5QVyGSpLlVCuqIOAx4BfDVesuRJD1cpfmoM/MB4Iiaa5EkzcErEyWpcAa1JBXOoJakwhnUklQ4g1qSCmdQS1LhDGpJKpxBLUldUn2Ku6UxqCWpS6Km7RrUklQ4g1qSCmdQS1LhDGpJKpxBLUmFM6glqXAGtSQVzqCWpMIZ1JJUOINakgpX9ea2j4mICyLihoi4PiJeVHdhkqSWSje3BT4OfDszXxcR64FDa6xJknSQRYM6Ih4NvAx4M0Bm7gX21luWJPWXrGvqPCByka1HxAnAJuA64NnANuCszHzgYctNABMAIyMjGycnJzsqaGpqiuHh4Y7W7Vf2vPoNWr8weD2fu2Oa7bv28fETO+t5fHx8W2aOzvliZi74BYwCM8AL2s8/DnxooXU2btyYndq8eXPH6/Yre179Bq3fzMHr+b0XbM9n/+M3Ol4f2JrzZGqVDxN3Ajsz8/L28wuA53b0vwxJ0pItGtSZ+WvglxHxjPY/nURrGESStAKqnvXxTuBz7TM+bgHeUl9JkqSDVQrqzLyK1li1JGmFeWWiJBXOoJakwhnUklQ4g1qSCmdQS1LhDGpJKpxBLUldUOekTAa1JHVJ1LRdg1qSCmdQS1LhDGpJKpxBLUmFM6glqXAGtSQVzqCWpMIZ1JJUOINakgpnUEtS4QxqSSpcpXsmRsStwP3ALDCTmd4/UZJWSNW7kAOMZ+adtVUiSX0sqW/6PIc+JKlLoqbp8yIrTKIaET8D7gESOCczN82xzAQwATAyMrJxcnKyo4KmpqYYHh7uaN1+Zc+r36D1C4PX86evmeaaO/bxHyd21vP4+Pi2+YaVqw59vDQzb4uIJwDfiYgbMvPSgxdoh/cmgNHR0RwbG+uo2EajQafr9it7Xv0GrV8YvJ6/eed2rr3rtlp6rjT0kZm3tf+7C7gQeH7XK5EkzWnRoI6IwyLi8P2PgVcCO+ouTJLUUmXoYwS4MFqj5GuBz2fmt2utSpJ0wKJBnZm3AM9egVokSXPw9DxJKpxBLUmFM6glqXAGtSQVzqCWpMIZ1JLUBRVm4+iYQS1JhTOoJalwBrUkFc6glqTCGdSSVDiDWpIKZ1BLUuEMakkqnEEtSYUzqCWpcAa1JBXOoJakwhnUklS4ykEdEUMRcWVEXFxnQZLUj2qcPG9JR9RnAdfXVYgk9bNmJlHTtisFdUQcDfwR8J811SFJ/S0hakrqyAqzXUfEBcA/A4cD78nMV8+xzAQwATAyMrJxcnKyo4KmpqYYHh7uaN1+Zc+r36D1C4PX8zlXP8iNd83w0fHOeh4fH9+WmaNzvbZ2sZUj4tXArszcFhFj8y2XmZuATQCjo6M5NjbvogtqNBp0um6/sufVb9D6hcHr+Wu/vpKb7rm9lp6rDH28BDg1Im4FJoETI+K/u16JJPWxpL6hj0WDOjPfl5lHZ+YG4HTgksx8Qz3lSFJ/yqS3HyZKkhaW1BfUi45RP6SQzAbQqKUSSepjzRoPqT2ilqRucOhDksqW9PiCF0nSwrLGQWqDWpK6wLM+JKlwDn1IUuGaCVHTFS8GtSR1QYVpkzpmUEtSVzj0IUlFyxqnOTWoJakLSrnDiyRpHplZW6Aa1JLUBU0veJGkstU5e55BLUldUOW2hp0yqCWpSzyilqSCeXqeJBXOuT4kqXDNpkfUklS0rPGSl0WDOiIeGRE/iojtEXFtRHywtmokqU/VOR91lZvbTgMnZuZURKwDvh8R38rMLTXVJEl9p85LyBcN6mydHDjVfrqu/VVnTZLUdzKTNTUdUkeVk7QjYgjYBjwVODsz3zvHMhPABMDIyMjGycnJjgqamppieHi4o3X7lT2vfoPWLwxezx/esochZnnfCzvreXx8fFtmjs71WqWgPrBwxGOAC4F3ZuaO+ZYbHR3NrVu3LrlQgEajwdjYWEfr9it7Xv0GrV8YvJ5PO/syZvfcz8XvObmj9SNi3qBe0lkfmflbYDPQWSWStEo1m8lQr07Pi4jHt4+kiYhDgFcAN9RTjiT1p5lmfWPUVc76eCJwfnuceg3wpcy8uJ5yJKk/NXsZ1Jl5NfCcenYvSavDbKZXJkpSyeo8ojaoJakLZnr5YaIkaXGzzWRNTWMfBrUkdUGzxisTDWpJ6oLZph8mSlLRmukYtSQVrc4LXgxqSeqCWYNaksrWbGZtgWpQS1IXtK5M9PQ8SSrWrBe8SFLZHKOWpIJlJs3EoJakUjXbN8oyqCWpULPtpPbKREkq1L7ZJgBra0pUg1qSlmnvTCuo19U09mFQS9IyTR8I6nq2b1BL0jJNz8wCPQzqiDgmIjZHxHURcW1EnFVPKZLUn6ZrHvqochfyGeBvM/PHEXE4sC0ivpOZ19VSkST1mQNj1EP1bH/RI+rMvD0zf9x+fD9wPXBUPeVIUv/ZP/SxtqbT8yIzqy8csQG4FDguM+972GsTwATAyMjIxsnJyY4KmpqaYnh4uKN1+5U9r36D1i8MVs/X3TXLR654kLOOT55zVGc9j4+Pb8vM0bleqzL0AUBEDANfAd718JAGyMxNwCaA0dHRHBsb66jYRqNBp+v2K3te/QatXxisnvMnu+CKK3jUYYfU0nOlzygjYh2tkP5cZn6161VIUh+b3tfj0/OiNcHqp4HrM/Nj9ZQhSf3rwBh1Dy94eQnwRuDEiLiq/XVKLdVIUh+q+4KXRceoM/P7QE2fZUpS/+v56XmSpIXVfcGLQS1Jy9TzS8glSQvbP/ThNKeSVKjpmSbrh9awxruQS1KZpvc1WV/X4TQGtSQt2/TMLI8wqCWpXHtnmga1JJVsesahD0kqWmvoo6arXTCoJWnZ9s40eURdJ1FjUEvSsk07Ri1JZXOMWpIK5xi1JBXO0/MkqXAOfUhS4ab3eUQtSUXbO9t0jFqSSja9z7k+JKloPR+jjohzI2JXROyorQpJ6lOzzWSmmT0f+jgPOLm2CiSpj+2/u0tPLyHPzEuBu2urQJL62P77Ja4fqi+oIzMXXyhiA3BxZh63wDITwATAyMjIxsnJyY4KmpqaYnh4uKN1+5U9r36D1i8MTs/3PNjk3Y09vOnY9TzvcdMd9zw+Pr4tM0fnem3tsio8SGZuAjYBjI6O5tjYWEfbaTQadLpuv7Ln1W/Q+oXB6fmXd++GxmaOf9YzGb7/plp69qwPSVqG/UMfnp4nSYV6cF/rw8Ren573BeCHwDMiYmdEnFlbNZLUZ6b3n/VRY1AvOkadmWfUtndJ6nMHTs9bO8R0Tftw6EOSluHA6XmOUUtSmVZi6MOglqRl2D/08UhvbitJZdp/RL1+yGlOJalIB86j9ohakso0vc8xakkq2t7Z352eVxeDWpKWYbqEKxMlSfObnpll7ZpgaE3Utg+DWpKWYe9Mk3U1zkUNBrUkLcu+2XrvlwgGtSQty97ZNKglqWR7Z5q13oYLDGpJWpZ9s03WDdX3QSIY1JK0LI5RS1LhPOtDkgq3d9aglqSiOfQhSYUr5qyPiDg5In4SETdFxN/VWpEk9ZF9s9n7sz4iYgg4G3gVcCxwRkQcW2tVktQnZprJ0Jp6j6gXvQs58Hzgpsy8BSAiJoHTgOu6XcybP/MjfnPHHs65ccu8yyRZaVtZbbGKW6u2YKe13XvvHj55/Q862SVZsdGqfXb1+7bAxu67fw//vuP71be18OYeulyFLVbeVpe+H1NTexje/r3K71elfXbx+1F1e9Xfq2T37t0cuq3Rle1V+/2ruKku/s5kwi/u3s2GIw6tuPfOVAnqo4BfHvR8J/CChy8UERPABMDIyAiNRmPJxdxx5x72zsxy1z33LLhct//IiIob7OZ+D97nmpxl99S99e+z6ta6uNh8yxyyZpZ8cGpJG4uK+6yq6raq/nwsZN26WYZmH6i8327s8yHb69pC1Wt79CFN1g49WG3hKvutskwPvm9PPWYtzxv+LY1Gg6mpqY6ybzFVgrqSzNwEbAIYHR3NsbGxJW9jbAwajQadrNvP7Hn1G7R+wZ67qcrAym3AMQc9P7r9b5KkFVAlqK8AnhYRvxcR64HTgYvqLUuStN+iQx+ZORMRfw38DzAEnJuZ19ZemSQJqDhGnZnfBL5Zcy2SpDl4ZaIkFc6glqTCGdSSVDiDWpIKF928pPXARiPuAH7e4epHAnd2sZx+YM+r36D1C/a8VE/OzMfP9UItQb0cEbE1M0d7XcdKsufVb9D6BXvuJoc+JKlwBrUkFa7EoN7U6wJ6wJ5Xv0HrF+y5a4obo5YkPVSJR9SSpIMY1JJUuJ4F9WI3zI2IR0TEF9uvXx4RG1a+yu6p0O/fRMR1EXF1RHw3Ip7cizq7qepNkSPitRGREdH3p3JV6Tki/qz9Xl8bEZ9f6Rq7rcLP9pMiYnNEXNn++T6lF3V2S0ScGxG7ImLHPK9HRHyi/f24OiKeu+ydZuaKf9GaLvVm4CnAemA7cOzDlvkr4FPtx6cDX+xFrSvY7zhwaPvx2/u536o9t5c7HLgU2AKM9rruFXifnwZcCTy2/fwJva57BXreBLy9/fhY4NZe173Mnl8GPBfYMc/rpwDfonUnrxcCly93n706oj5ww9zM3Avsv2HuwU4Dzm8/vgA4KaLbd0RbMYv2m5mbM3N3++kWWnfS6WdV3mOADwH/CnTv5nq9U6XnvwTOzsx7ADJz1wrX2G1Vek7gUe3HjwZ+tYL1dV1mXgrcvcAipwH/lS1bgMdExBOXs89eBfVcN8w9ar5lMnMGuBc4YkWq674q/R7sTFr/R+5ni/bc/pPwmMz8xkoWVqMq7/PTgadHxGURsSUiTl6x6upRpecPAG+IiJ205rV/58qU1jNL/X1fVNdubqvuiIg3AKPAH/a6ljpFxBrgY8Cbe1zKSltLa/hjjNZfTZdGxPGZ+dueVlWvM4DzMvOjEfEi4LMRcVxmNntdWL/o1RF1lRvmHlgmItbS+pPprhWprvsq3SA4Il4OvB84NTOnV6i2uizW8+HAcUAjIm6lNZZ3UZ9/oFjlfd4JXJSZ+zLzZ8CNtIK7X1Xp+UzgSwCZ+UPgkbQmL1qtun5D8F4FdZUb5l4EvKn9+HXAJdkeqe9Di/YbEc8BzqEV0v0+bgmL9JyZ92bmkZm5ITM30BqXPzUzt/am3K6o8nP9NVpH00TEkbSGQm5ZySK7rErPvwBOAoiIZ9IK6jtWtMqVdRHwF+2zP14I3JuZty9riz385PQUWkcTNwPvb//bP9H6ZYXWm/ll4CbgR8BTev1pb839/i/wG+Cq9tdFva657p4ftmyDPj/ro+L7HLSGfK4DrgFO73XNK9DzscBltM4IuQp4Za9rXma/XwBuB/bR+gvpTOBtwNsOeo/Pbn8/runGz7WXkEtS4bwyUZIKZ1BLUuEMakkqnEEtSYUzqCWpcAa1JBXOoNaqFBFjEfHimvdxa/uiFalWBrVWqzFgSUHdnqpAKo5BrWJFxIaIuCEizouIGyPicxHx8vbMcz+NiOdHxOMi4mvtCdq3RMTvt28y8Tbg3RFxVUT8QXtblxx0Y4YntfdxXkR8KiIuBz4yTx3DEfGZiLimvf5rV+ybIOHseSrfU4E/Bd5Ka16JPwdeCpwK/D2t6SSvzMzXRMSJtOYBPiEiPgVMZea/AUTE14HzM/P8iHgr8AngNe19HA28ODNn56nhH2jN13B8e1uPraNRaT4GtUr3s8y8BiAirgW+m5kZEdcAG4AnA68FyMxLIuKIiHjUHNt5EfAn7cef5aFHz19eIKQBXk5rsiHa+7mn02akTjj0odIdPN1r86DnTbp3oPFAl7Yj1cKgVr/7HvB6aJ3pAdyZmfcB99Oa83q/H/C7o+LXt9er6jvAO/Y/cehDK82gVr/7ALAxIq4G/oXfzWH+deCP93+YSOv2T29pL/dG4Kwl7OPDwGMjYkdEbKd1I2JpxTjNqSQVziNqSSqcZ31IbRHxFv7/kMhlmfmOuZaXVopDH5JUOIc+JKlwBrUkFc6glqTCGdSSVLj/A2RDB9EFr3xFAAAAAElFTkSuQmCC\n"
+          },
+          "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+wf5rNhSRpgxXHFmf26dmsT4AV+bbFPEUQpwEaDR42VZTgnPr93JxsJKenSJ5M60Ydx0bhJx3bU0mQQmBbiEtOrDjfzzsz288GEBRQfrGNInmv+6ehzXTkyka5T+eUhg03+hEpJKKut44cMCFny6m+r6JiYNieWRK8dy0Rn9NDeJuIYCXEJKXkkVz67JZ+nGYiwwfVw8t58/lDMHqn9b3EcBLkHPWsvHO/bx9Jp8Vn+xl65R4Xz3nMHcdt4QBsZqUilxLwW4BC2P17I8t5Sn1+xgU2ElfWI68bNLRnHj5EFfjt8WcTMFuASdw40eXssu5Nk1+RTsqyWpd1d+NzOZayYm0jlSswFK8FCAS9CoPtzI/E9389zaneytrmf8gB789caJXDI2XvOTSFBSgIvr7aup54UPC5j3cQFVh5s4b3gfnrxhAucO6621JSWoKcDFtUoq63hmTT4L1u2mvsnLJWPiuTNtmEaUSMhQgIvrFFQc4qnVO3gtuxCvhasnJHJn2jCG94txujSRDqUAF9fYXl7NX1bt4I0NRUSEhzFr0iBuP3+ohgJKyFKAS8DLK6niz+9v453cUjpHhHPbeUO4/fyh9NMcJRLiFOASsHKLKvnz+9t4d3MZMZ0iuCttGLedN5RYLVUmAijAJQDlFFby5MoveC+vnG6dI7j3ohHMnjqEHl0jnS5NJKAowCVg5BZV8sR7zcHdvXME900byc1TkujRRcEtciwKcHHc5uJK/rRiG+/lldG9cwT3TxvJzVOT6N5ZwS1yIgpwcczW0mr+tOILlm8u/fKK+xYFt0irKcClw20vr+GJ977g7ZwSYqJ8fdznDVFXicgpUoBLh9mzv5YnV25jcXYhnSPDufMbw5hzwVDNDCjSRgpwaXflVYeZt6WeD1ZkYIxh9tQh/CBtGH1iOjldmoirnTTAjTHPA5cD5dbacb5tjwC3A3t9b3vQWrusvYoUd6qsbeRvq3fw4kc7aWzy8u1Jg/jhhSOI76EbcET8oTVX4C8C/wfMO2r7n6y1f/B7ReJ6tQ1NvPBhAU+t3kFNfRNXndmfKd0PcP2MZKdLEwkqJw1wa+0aY0xS+5cibtfo8bIocw9PvLeNvdX1XDy6Hz+9ZBRnxHcnIyPD6fJEgo6x1p78Tc0B/tZRXSi3AFVAJnC/tfbAcfadA8wBiIuLS1m4cGGbCq2pqSEmJnhnm3Nz+6y1ZJZ5ePWLBspqLSN6hnH9qChG9Pr36jdubl9rqH3uFujtS09Pz7LWpn7tBWvtSb+AJCC3xfM4IBwIA34LPN+a46SkpNi2WrVqVZv3dQO3tm/dzn326r+stYN//pad9scMu2JzqfV6vV97n1vb11pqn7sFevuATHuMTG3TKBRrbdmRx8aYZ4G32nIcca8de2t47J3PWbGljLjunXj8W+O5duIALV0m0oHaFODGmARrbYnv6Uwg138lSSCrqKnnyfe28fK63XSJDOdnl4xi9tQhdInSYsEiHa01wwgXAGlAH2NMIfBrIM0YMwGwQAFwRzvWKAHgcKOH5z/cyV9X7aCu0cONkwfxo4tGaCy3iINaMwpl1jE2P9cOtUgAstby1qYSHnvnc4oO1nHx6H48MH20li8TCQC6E1OOa+Oeg/zmrS1k7TrA6ITu/Pe3xjNleB+nyxIRHwW4fE151WF+v3wrr2UX0icmiseuSea61IH6gFIkwCjA5Uv1TR6eW7uTv7y/nQaPlzu+MZR70ofTTdO7igQkBbgAsDKvjN+8tYVd+2q5eHQcD182mqQ+0U6XJSInoAAPcQUVh/jPNzezautehvaNZt7sSVwwsq/TZYlIKyjAQ1Rdg4e/Zmzn6dX5REWE8dCM0dw8JYmoiDCnSxORVlKAhxhrLSu2lPGfb26h6GAdV0/oz4MzRtOvu6Z4FXEbBXgI2bO/lkeWbmbl5+WMjIth4ZxzOGdob6fLEpE2UoCHgIYmL89+kM+f399GmDE8NGM0t0xNIjJc3SUibqYAD3Lrdu7noSU5bCuv4dKx8fzqijH079nF6bJExA8U4EGqsraRR9/JY+Fne0js2YXnbk7lotFxTpclIn6kAA8y1lrezinhkaVbOFDbwJwLhvLji0fQNUqnWiTY6F91ECk+WMcvX89l5eflJCf24MVbz2ZcYg+nyxKRdqIADwJer2XBZ7t5dNnneLyWhy8bzS1TkojQh5QiQU0B7nK79h3i569t4pP8/Uwd3ptHZ45nUO+uTpclIh1AAe5SXq9l7scFPL58KxFhhseuSeaGswdijGYMFAkVCnAXKqg4xH+8uol1BftJH9WX312TTEIPDQ0UCTUKcBfxei3/+HQXjy77nIhwwx+uO5NrJybqqlskRCnAXaL4YB3/8eom1m6v4IKRffn9tbrqFgl1CvAAZ63l9Q1F/OqNzXi8lt/NTGbWJPV1i4gCPKAdrG3goSW5vJ1TQsrgXvzx+jMZ3FuLLIhIMwV4gPpoewX3LdpIRU09P7tkFD/4xjCtSSkiX6EADzANTV7+Z8VWnlmTz5De0Sy5ayrJA3Q3pYh8nQI8gBRUHOJHC9ezqbCSWZMG8cvLR2sOExE5LqVDgPiouIm73/+AiPAwnvruRC4dl+B0SSIS4BTgDqtr8PDrpbks2lTPpKRYnvj2BM3XLSKtogB30Pbyau6an8228hquGBbJn2ZP1gRUItJqCnCHvL6+iF8sziG6UzgvzZ5MU1GuwltETokCvIPVN3n4zZtbmP/pbiYlxfLn75xFXPfOZBQ5XZmIuI0CvAMVH6zjzn9ksbGwkjsuGMrPLhmlq24RaTMFeAf5aHsF9yxYT0OTl6e+m8Kl4+KdLklEXO6kl3/GmOeNMeXGmNwW22KNMSuMMdt833u1b5nuZa3l7x/k893nPiU2Ooo37pmq8BYRv2jN3+8vApcete0BYKW1dgSw0vdcjnK40cN9izby/97OY9qYOF6/eyrD+sY4XZaIBImTdqFYa9cYY5KO2nwVkOZ7PBfIAH7ux7pcr6zqMHPmZbKxsJL7p43k7vThhGkuExHxI2OtPfmbmgP8LWvtON/zg9banr7HBjhw5Pkx9p0DzAGIi4tLWbhwYZsKrampISbGHVevOys9PJldT12T5Y7xnZgYd/KPGtzUvrZQ+9xN7XNWenp6lrU29WsvWGtP+gUkAbktnh886vUDrTlOSkqKbatVq1a1ed+OtGxTsR318DI75dGVdktxZav3c0v72krtcze1z1lApj1GprZ1FEqZMSbBWltijEkAytt4nKBhreVvq3fw+PKtTBzUk2duSqVPTCenyxKRINbWQchLgZt9j28G3vBPOe7U5PHy4JIcHl++lSvP7M/Lt5+j8BaRdnfSK3BjzAKaP7DsY4wpBH4NPAYsMsbcBuwCrm/PIgPZofom7pqfzeov9nJP+nDu/+ZILXcmIh2iNaNQZh3npYv8XIvr7K2uZ/aLn7GlpIpHr0lm1qRBTpckIiFEd2K20a59h7jp+XWUVR3m2ZtSuPCMOKdLEpEQowBvg7ySKm56fh2NHi8v334OEwfpRlQR6XgK8FOUtWs/t7zwGTGdIlhw+7kM79fN6ZJEJEQpwE/B2m0V3D4vk4QenXnp+5NJ1Mo5IuIgBXgrvbeljLvmZzO0bzQv3TaZvt00TFBEnKUAb4XluaX8cEE2YxK6M3f2JHp2jXK6JBERBfjJvJNTwg8XrGf8gB68OHsS3TtHOl2SiAigAD+h5ivv9UwY2JMXZ08ippN+XSISOLSe13Gs2FLGPS9nf3nlrfAWkUCjAD+GD7bt5e752YxN7MFchbeIBCgF+FEyC/Zz+7xMhvWLYd6tk+imPm8RCVAK8Ba2FFdx64uf0b9nF166bRI9uiq8RSRwKcB9du+r5abn1xHTKYKXbpus6WBFJOApwIF9NfXc/MI6mrxeXrptku6wFBFXCPkAr2vwcNvcTIoP1vHczama20REXCOkh1d4vZb7Fm1gY+FBnvpuCimDY50uSUSk1UL6Cvzxd7fyTm4pD80YzSVj450uR0TklIRsgL+WVchTq3dw4+RB3HbeEKfLERE5ZSEZ4Ot3H+AXi3M4d2hvHrlyrNawFBFXCrkA31tdzw/+kUVcj0789caJRIaH3K9ARIJESH2I2ejxcvfL2VTWNbL4zqn0ita0sCLiXiEV4H/411bW7dzPEzdMYEz/7k6XIyJyWkKm/2BlXhlPr87nxsmDuPqsRKfLERE5bSER4KWVh/npKxsZ2787v7x8jNPliIj4RdAHuNdr+ck/N3C40cv/zjqLzpHhTpckIuIXQd8H/tzanXycv4/fX5vMsL4xTpcjIuI3QX0FvrW0mv9+dyvTxsRxfepAp8sREfGroA3wJo+Xn76ykW6dI3j0mmTdrCMiQSdou1CeXpNPTlElf71xoub2FpGgFJRX4AUVh3hy5Tamj4tnRnKC0+WIiLSL07oCN8YUANWAB2iy1qb6o6jTYa3l4ddz6RQexiNXjnW6HBGRduOPLpR0a22FH47jF2/nlLB2ewW/uWoscd07O12OiEi7CaoulNqGJn77dh5jErpz4+TBTpcjItKujLW27TsbsxM4AFjgaWvtM8d4zxxgDkBcXFzKwoUL2/SzampqiIk58TjuJdsaeGNHIw9N7syIXu66Yac17XMztc/d1D5npaenZx2zi9pa2+YvINH3vR+wEbjgRO9PSUmxbbVq1aoTvl58sNaOeniZvWt+Vpt/hpNO1j63U/vcTe1zFpBpj5Gpp9WFYq0t8n0vB5YAk07neKfjyfe24fFaHrj0DKdKEBHpUG0OcGNMtDGm25HHwDeBXH8Vdiry99bwSlYhN04ezMDYrk6UICLS4U5nFEocsMR3h2ME8LK1drlfqjpFf35/O1HhYdydPtyJHy8i4og2B7i1Nh8404+1tMmOvTW8saGI288fSt9uuuNSREKH64cRPr16B1ERYdx+wVCnSxER6VCuDvDSysMsWV/E9akDNd+JiIQcVwf4vI8L8Hgt3z9PV98iEnpcG+CHGz0sWLebi0fHMai3Rp6ISOhxbYC/vamEA7WN3DIlyelSREQc4doAX7BuN0P6RHPusN5OlyIi4ghXBnj+3hoydx3ghrMHaqUdEQlZrgzwJeuLCDMw86xEp0sREXGM6wLcWsvSjcVMHd5H832LSEhzXYBvLq5i175aLh+vpdJEJLS5LsD/tbmUMAPTxsQ7XYqIiKNcF+Ar8spJHRxLbHSU06WIiDjKVQFeXnWYvJIq0s/o53QpIiKOc1WAr93evHby+SP6OFyJiIjzXBXgH+/YR8+ukYxJ6O50KSIijnNVgH9WsJ/UwbGEhenmHRER1wR4dYOlYF8tKYN7OV2KiEhAcE2A76z0AHDmwB4OVyIiEhhcE+C7q70AjO2vABcRARcFeFG1l/49OtOjS6TTpYiIBATXBHjJIcuwfjFOlyEiEjBcEeDWWspqvQzpE+10KSIiAcMVAV5V10RdEwyK1dJpIiJHuCLAiyvrAEjo0cXhSkREAocrAnxvdT0A/bp3crgSEZHA4YoA33+oAUAzEIqItOCKAK+sawTQEEIRkRZcEeA19U0AdOsc4XAlIiKBwxUBXtvQhAE6RYQ7XYqISMBwRYA3NHmJVHaLiHyFKwK80WMJ1wyyIiJfcVoBboy51Biz1Riz3RjzgL+KOpq1Fk0BLiLyVW0OcGNMOPAXYDowBphljBnjr8Ja8lpQfouIfNXpXIFPArZba/OttQ3AQuAq/5T1dQpwEZGvOp1xeYnAnhbPC4HJR7/JGDMHmAMQFxdHRkbGKf+gpoONDI6xbdrXLWpqatQ+F1P73M2t7Wv3gdXW2meAZwBSU1NtWlraKR8jDcjIyKAt+7qF2uduap+7ubV9p9OFUgQMbPF8gG+biIh0gNMJ8M+AEcaYIcaYKODbwFL/lCUiIifT5i4Ua22TMeYe4F0gHHjeWrvZb5WJiMgJnVYfuLV2GbDMT7WIiMgpcMWdmCIi8nUKcBERl1KAi4i4lAJcRMSljLW2436YMXuBXW3cvQ9Q4cdyAo3a525qn7sFevsGW2v7Hr2xQwP8dBhjMq21qU7X0V7UPndT+9zNre1TF4qIiEspwEVEXMpNAf6M0wW0M7XP3dQ+d3Nl+1zTBy4iIl/lpitwERFpQQEuIuJSrgjwjlo82SnGmAJjTI4xZoMxJtPpek6XMeZ5Y0y5MSa3xbZYY8wKY8w23/deTtZ4Oo7TvkeMMUW+c7jBGDPDyRpPhzFmoDFmlTFmizFmszHmXt/2oDiHJ2if685hwPeB+xZP/gKYRvOybZ8Bs6y1WxwtzI+MMQVAqrU2kG8kaDVjzAVADTDPWjvOt+1xYL+19jHf/4R7WWt/7mSdbXWc9j0C1Fhr/+Bkbf5gjEkAEqy12caYbkAWcDVwC0FwDk/Qvutx2Tl0wxV4hy6eLKfPWrsG2H/U5quAub7Hc2n+B+NKx2lf0LDWllhrs32Pq4E8mtfADYpzeIL2uY4bAvxYiye78pd9Ahb4lzEmy7cIdDCKs9aW+B6XAnFOFtNO7jHGbPJ1sbiye+Foxpgk4CzgU4LwHB7VPnDZOXRDgIeC86y1E4HpwN2+P9GDlm3utwvsvrtT9zdgGDABKAH+x9lyTp8xJgZ4Dfixtbaq5WvBcA6P0T7XnUM3BHjQL55srS3yfS8HltDcbRRsynx9j0f6IMsdrsevrLVl1lqPtdYLPIvLz6ExJpLmcJtvrV3s2xw05/BY7XPjOXRDgAf14snGmGjfBykYY6KBbwK5J97LlZYCN/se3wy84WAtfnck2Hxm4uJzaIwxwHNAnrX2jy1eCopzeLz2ufEcBvwoFADfcJ4n+Pfiyb91uCS/McYMpfmqG5rXKH3Z7e0zxiwA0mieorMM+DXwOrAIGETzlMLXW2td+UHgcdqXRvOf3hYoAO5o0V/sKsaY84APgBzA69v8IM39xK4/hydo3yxcdg5dEeAiIvJ1buhCERGRY1CAi4i4lAJcRMSlFOAiIi6lABcRcSkFuIiISynAJWQZY9KMMVP8dKxbjDH/549jibSWAlxCWRpwSgFujIlon1JETp0CXFzNGJNkjPncGISWj9MAAAFfSURBVPOiMeYLY8x8Y8zFxpgPfQsPTPItRPC6b5a5T4wx432z0P0A+Ilv8v7zfcd63/e+lcaYQb6f8aIx5iljzKfA4w42V+QrdDUhwWA4cB0wm+a5c74DnAdcSfMt0nuA9dbaq40xF9K8EMMEY8xTtJjA3xjzJjDXWjvXGDMb+F/+Pef1AGCKtdbTkQ0TORFdgUsw2GmtzfHNIrcZWOmb7jQHSKI5zF8CsNa+D/Q2xnQ/xnHOBV72PX7Jt98Rryi8JdAowCUY1Ld47G3x3Iv//so85KfjiPiNAlxCwQfAjdA88gSo8E3gXw10a/G+j2ierhjf+z/owBpFTpkCXELBI0CKMWYT8Bj/ntP6TWDmkQ8xgR8Ct/re9z3gXieKFWktTScrIuJSugIXEXEpDSMUOQXGmFv5etfKh9bau52oR0KbulBERFxKXSgiIi6lABcRcSkFuIiISynARURc6v8D3gn7L7krbOQAAAAASUVORK5CYII=\n"
+          },
+          "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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\n"
+          },
+          "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"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file