{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "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": "84e97c6d-94d0-421e-8095-e729c9a34db8"
      },
      "execution_count": 1,
      "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 31.2 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=2021298 sha256=ef6b71965ab3a32f21fe1daea1abbd06cfbfa806c45e9a65c6796ef179774339\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 31.3 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: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->control) (1.4.4)\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: 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: 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=b7dbf248d6db2ffa075018978d031e1b619234352ae5cc429fa52fd4ae0e9685\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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlHXy4vZR402",
        "outputId": "27da5aad-c71a-4dfb-91c4-04e9c4fd091b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2790.4639871211475\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",
        "import time\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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start = time.time()\n",
        "\n",
        "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\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "print(\"Time elapsed: \", end - start, \"s\")"
      ],
      "metadata": {
        "id": "0fC4tNGBxRx9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "772570c9-dd1f-4b1d-c299-6bf5ec7b3265"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time elapsed:  228.38785576820374 s\n"
          ]
        }
      ]
    },
    {
      "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": 5,
      "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": 6,
      "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": "bbb8b18a-65b3-4cfe-a354-75556ee0a332"
      },
      "execution_count": 7,
      "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": "c8a7eee2-b2ba-43a2-b64e-a00f0312e964"
      },
      "execution_count": 8,
      "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_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": "2dbc182a-8300-4285-9b33-fcdc2e228b6a"
      },
      "execution_count": 9,
      "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/(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": "03dc4e63-5a72-4f6e-c4b3-d84884101188"
      },
      "execution_count": 10,
      "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": "4c56bc20-f94e-43ce-9f1f-697da03d2231"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% overshoot :  0.0 %\n",
            "Rise time :  1.1315389526151183\n",
            "%Settling time : -\n",
            "\n",
            "% overshoot :  0.15210430606766856 %\n",
            "Rise time :  0.005695236394454569\n",
            "%Settling time : -\n"
          ]
        }
      ]
    }
  ]
}