2nd_order_Process_Identification_with_Tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# ProcessIdentification class\n",
    "# Necessary libraries\n",
    "import numpy as np\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from datetime import datetime\n",
    "he_init = tf.variance_scaling_initializer()\n",
    "now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n",
    "\n",
    "class SecondOrderProcessIdentification:\n",
    "    def __init__(self, n_hidden_layers=2, n_neurons=100, optimizer_class=tf.train.MomentumOptimizer,\n",
    "                 momentum = 0.9, learning_rate=0.01, n_inputs = 7, n_outputs = 1, batch_size=50, activation=tf.nn.relu,\n",
    "                 initializer = None, learning_object=None, loaded_data=None, random_state=None):\n",
    "        self.n_hidden_layers = n_hidden_layers\n",
    "        self.n_neurons = n_neurons\n",
    "        self.optimizer_class = optimizer_class\n",
    "        self.momentum = momentum\n",
    "        self.n_inputs = n_inputs\n",
    "        self.n_outputs = n_outputs\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = batch_size\n",
    "        self.activation = activation\n",
    "        self.initializer = initializer        \n",
    "        self.learning_object = learning_object\n",
    "        self.loaded_data = loaded_data\n",
    "        self.random_state = random_state\n",
    "        self._session = None\n",
    "        \n",
    "    def data_preparation(self):\n",
    "        train_set, test_set = train_test_split(self.loaded_data, test_size=0.2, random_state=42)\n",
    "        X_train, y_train = train_set.iloc[:,:7].values, train_set.iloc[:,7:].values\n",
    "        X_test, y_test = test_set.iloc[:,:7].values, test_set.iloc[:,7:].values\n",
    "        X_scale_minmax = X_train\n",
    "        scaler = MinMaxScaler()\n",
    "        scaler.fit(X_train)\n",
    "        X_train = scaler.transform(X_train)\n",
    "        X_test = scaler.transform(X_test)   \n",
    "        return X_train, y_train, X_test, y_test, X_scale_minmax\n",
    "    \n",
    "    def data_preparation_theta(self):\n",
    "        train_set, test_set = train_test_split(self.loaded_data, test_size=0.2, random_state=42)\n",
    "        X_train, y_train = train_set.iloc[:,:17].values, train_set.iloc[:,17:].values\n",
    "        X_test, y_test = test_set.iloc[:,:17].values, test_set.iloc[:,17:].values\n",
    "        X_scale_minmax = X_train\n",
    "        scaler = MinMaxScaler()\n",
    "        scaler.fit(X_train)\n",
    "        X_train = scaler.transform(X_train)\n",
    "        X_test = scaler.transform(X_test)   \n",
    "        return X_train, y_train, X_test, y_test, X_scale_minmax\n",
    "    \n",
    "    def operating_data_preparation(self, X_scale_minmax):\n",
    "        X_op, y_op = self.loaded_data.iloc[:,:7].values, self.loaded_data.iloc[:,7:].values\n",
    "        process_gain = X_op[0,:]        \n",
    "        scaler = MinMaxScaler()\n",
    "        scaler.fit(X_scale_minmax)\n",
    "        X_op = scaler.transform(X_op)\n",
    "        return X_op, y_op, process_gain\n",
    "    \n",
    "    def operating_data_preparation_theta(self, X_scale_minmax):\n",
    "        X_op, y_op = self.loaded_data.iloc[:,:17].values, self.loaded_data.iloc[:,17:].values\n",
    "        scaler = MinMaxScaler()\n",
    "        scaler.fit(X_scale_minmax)\n",
    "        X_op = scaler.transform(X_op)\n",
    "        return X_op, y_op\n",
    "\n",
    "    def _dnn(self, inputs):\n",
    "        for layer in range(self.n_hidden_layers):\n",
    "            inputs = tf.layers.dense(inputs, self.n_neurons, kernel_initializer=self.initializer,\n",
    "                                     name=\"hidden%d\" % (layer + 1))\n",
    "            inputs = self.activation(inputs, name=\"hidden%d_out\" % (layer + 1))\n",
    "        return inputs\n",
    "    \n",
    "    def _build_graph(self, n_inputs, n_outputs):\n",
    "        if self.random_state is not None:\n",
    "            tf.set_random_seed(self.random_state)\n",
    "            np.random.seed(self.random_state)\n",
    "            \n",
    "        X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n",
    "        y = tf.placeholder(tf.float32, shape=(None), name=\"y\")\n",
    "        \n",
    "        dnn_outputs = self._dnn(X)\n",
    "        \n",
    "        y_predict = tf.layers.dense(dnn_outputs, n_outputs, name=\"outputs\")\n",
    "        \n",
    "        RMSE = tf.sqrt(tf.losses.mean_squared_error(y, y_predict), name=\"loss\")\n",
    "        \n",
    "        optimizer = self.optimizer_class(learning_rate=self.learning_rate, momentum=self.momentum, use_nesterov=True)\n",
    "        training_op = optimizer.minimize(RMSE)\n",
    "        \n",
    "        accuracy = tf.reduce_mean(1-(abs(y_predict - y)/y), name=\"accuracy\")\n",
    "        \n",
    "        init = tf.global_variables_initializer()\n",
    "        saver = tf.train.Saver()\n",
    "        \n",
    "        self._X, self._y = X, y\n",
    "        self._y_predict, self._RMSE = y_predict, RMSE\n",
    "        self._training_op, self._accuracy = training_op, accuracy\n",
    "        self._init, self._saver = init, saver\n",
    "\n",
    "    def close_session(self):\n",
    "        if self._session:\n",
    "            self._session.close()\n",
    "\n",
    "    def fit(self, X, y, n_epochs=20):\n",
    "        self.close_session()\n",
    "        \n",
    "        self._graph = tf.Graph()\n",
    "        with self._graph.as_default():\n",
    "            self._build_graph(self.n_inputs, self.n_outputs)        \n",
    "        \n",
    "        self._session = tf.Session(graph=self._graph)\n",
    "        \n",
    "        root_logdir = \"tf_logs_2nd_%s_RMSE\" % (self.learning_object)\n",
    "        logdir = \"{}/run-{}/\".format(root_logdir, now)\n",
    "        RMSE_summary = tf.summary.scalar('RMSE', self._RMSE)\n",
    "        file_writer = tf.summary.FileWriter(logdir, self._session.graph)\n",
    "        \n",
    "        with self._session.as_default() as sess:\n",
    "            self._init.run()\n",
    "            for epoch in range(n_epochs):\n",
    "                rnd_idx = np.random.permutation(len(X))\n",
    "                for rnd_indices in np.array_split(rnd_idx, len(X) // self.batch_size):\n",
    "                    X_batch, y_batch = X[rnd_indices], y[rnd_indices]\n",
    "                    feed_dict = {self._X: X_batch, self._y: y_batch}\n",
    "                    sess.run(self._training_op, feed_dict=feed_dict)\n",
    "                acc_batch = self._accuracy.eval(feed_dict=feed_dict)\n",
    "                RMSE_batch = self._RMSE.eval(feed_dict=feed_dict)\n",
    "                summary_str = RMSE_summary.eval(feed_dict=feed_dict)\n",
    "                file_writer.add_summary(summary_str, epoch)\n",
    "                print(\"{}\\tbatch data accuracy: {:.4f}\\tbatch data RMSE: {:.6f}\".format(epoch, acc_batch, RMSE_batch))\n",
    "            \n",
    "            save_path = self._saver.save(sess, \"./2nd_order_process_%s_learning.ckpt\" % self.learning_object)\n",
    "            file_writer.close()\n",
    "            \n",
    "    def predict(self, X, y, learning_object=None):\n",
    "        self._graph = tf.Graph()\n",
    "        with self._graph.as_default():\n",
    "            self._build_graph(self.n_inputs, self.n_outputs)\n",
    "            \n",
    "        self._session = tf.Session(graph=self._graph)\n",
    "        with self._session.as_default() as sess:\n",
    "            self._saver.restore(sess, \"./2nd_order_process_%s_learning.ckpt\" % self.learning_object)\n",
    "            accuracy_val = self._accuracy.eval(feed_dict={self._X: X, self._y: y})\n",
    "            X_new_scaled = X[:10]\n",
    "            y_pred = self._y_predict.eval(feed_dict={self._X: X_new_scaled})\n",
    "            \n",
    "            print(\"\\n\")\n",
    "            print(\"%s test data accuracy:\" % self.learning_object, accuracy_val)\n",
    "            print(\"\\n\")\n",
    "        \n",
    "        return y_pred\n",
    "    \n",
    "    def operating_predict(self, X, y, learning_object=None):\n",
    "        self._graph = tf.Graph()\n",
    "        with self._graph.as_default():\n",
    "            self._build_graph(self.n_inputs, self.n_outputs)\n",
    "            \n",
    "        self._session = tf.Session(graph=self._graph)\n",
    "        with self._session.as_default() as sess:\n",
    "            self._saver.restore(sess, \"./2nd_order_process_%s_learning.ckpt\" % self.learning_object)\n",
    "            X_new_scaled = X[:1]\n",
    "            y_pred = self._y_predict.eval(feed_dict={self._X: X_new_scaled})\n",
    "\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PID Tuning class    \n",
    "class PIDTuning:\n",
    "    def __init__(self, tau=None, zeta=None, theta=None, PID_type=None,\n",
    "                 tau_modeling_error = 0, zeta_modeling_error = 0,\n",
    "                 theta_modeling_error = 0, theta_tau_ratio_error = 0):\n",
    "        self.tau = tau*(1+tau_modeling_error)\n",
    "        self.zeta = zeta\n",
    "        self.theta = theta*(1+tau_modeling_error)*(1+theta_tau_ratio_error)\n",
    "        self.PID_type = PID_type\n",
    "\n",
    "    def IMC_tuning_PIPC(self, process_gain=None):\n",
    "        lamda = 0.25 * self.theta\n",
    "        if self.PID_type == \"PI\":\n",
    "            controller_gain = ((2*self.tau + self.theta)/2*lamda)/process_gain\n",
    "            integral_time = self.tau + self.theta/2\n",
    "            print(\"PI Controller by IMC Tuning_PIPC: Kc = %f, tau_i = %f\" % (controller_gain, integral_time))\n",
    "        elif self.PID_type == \"PID\":\n",
    "            controller_gain = ((2*self.tau + self.theta)/2*(lamda + self.theta))/process_gain\n",
    "            integral_time = self.tau + self.theta/2\n",
    "            derivative_time = self.tau*self.theta/(2*self.tau + self.theta)\n",
    "            print(\"PID Controller by IMC Tuning_PIPC: Kc = %f, tau_i = %f, tau_d = %f\" %\n",
    "                  (controller_gain, integral_time, derivative_time))\n",
    "            \n",
    "    def IMC_tuning_PDCT(self, process_gain=None, Condition=None):\n",
    "        if Condition == \"aggressive control\":\n",
    "            tau_c = 0.5/self.theta\n",
    "        elif Condition == \"default value\":\n",
    "            tau_c = self.theta\n",
    "        elif Condition == \"smooth control\":\n",
    "            tau_c = 1.5*self.theta\n",
    "        elif Condition == \"robust control\":\n",
    "            tau_c = 3*self.theta\n",
    "        controller_gain = 2*self.tau*self.zeta/((tau_c+self.theta)*process_gain)\n",
    "        integral_time = 2*self.tau*self.zeta\n",
    "        derivative_time = self.tau/2*self.zeta\n",
    "        print(\"PID Controller by IMC Tuning_PDCT: Kc = %f, tau_i = %f, tau_d = %f\" %\n",
    "             (controller_gain, integral_time, derivative_time))\n",
    "    \n",
    "    def ITAE_2_tuning(self, process_gain=None):\n",
    "        theta_tau_ratio = self.theta/self.tau\n",
    "        if self.zeta <= 0.9:\n",
    "            controller_gain_set_point = (-0.04 + (0.333 + 0.949*(theta_tau_ratio**(-0.983)))*self.zeta)/process_gain\n",
    "        else:\n",
    "            controller_gain_set_point = (-0.544 + 0.308*theta_tau_ratio + 1.408*(theta_tau_ratio**(-0.832))*self.zeta)/process_gain\n",
    "        if theta_tau_ratio <= 1.0:\n",
    "            integral_time_set_point = (2.055 + 0.072*theta_tau_ratio)*self.zeta*self.tau\n",
    "        else:\n",
    "            integral_time_set_point = (1.768 + 0.329*theta_tau_ratio)*self.zeta*self.tau\n",
    "        derivative_time_set_point = self.tau/((1.0 - np.exp(-(theta_tau_ratio**(1.060))*self.zeta/0.870))*\\\n",
    "        (0.55+1.683*(theta_tau_ratio**(-1.090))))\n",
    "        if self.zeta <= 0.9:\n",
    "            controller_gain_disturbance = (-0.670 + 0.297*(theta_tau_ratio**(-2.001)) +\n",
    "                                           2.189*(theta_tau_ratio**(-0.766))*self.zeta)/process_gain\n",
    "        else:    \n",
    "            controller_gain_disturbance = (-0.365 + 0.260*((theta_tau_ratio - 1.400)**(2)) +\n",
    "                                           2.189*(theta_tau_ratio**(-0.766))*self.zeta)/process_gain            \n",
    "        if theta_tau_ratio < 0.4:\n",
    "            integral_time_disturbance = (2.212*(theta_tau_ratio**(0.520)) - 0.300)*self.tau\n",
    "        else:\n",
    "            integral_time_disturbance = (-0.975 + 0.910*((theta_tau_ratio - 1.845)**(2)) +\n",
    "                                         (1 - np.exp(-self.zeta/(0.150 + 0.330*theta_tau_ratio)))*\n",
    "                                         (5.250 - 0.880*((theta_tau_ratio - 2.800)**(2))))\n",
    "        derivative_time_disturbance = self.tau/(-1.900 + 1.576*(theta_tau_ratio**(-0.530)) +\n",
    "                                                (1 - np.exp(-self.zeta/(-0.15 + 0.939*(theta_tau_ratio**(-1.121)))))*\n",
    "                                                (1.45 + 0.969*(theta_tau_ratio**(-1.171))))\n",
    "        print(\"PID Controller by ITAE-2-setpoint tuning: Kc = %f, tau_i = %f, tau_d = %f\" %\n",
    "              (controller_gain_set_point, integral_time_set_point, derivative_time_set_point))\n",
    "        print(\"PID Controller by ITAE-2-disturbance tuning: Kc = %f, tau_i = %f, tau_d = %f\" %\n",
    "              (controller_gain_disturbance, integral_time_disturbance, derivative_time_disturbance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data load for training\n",
    "learning_2nd = pd.read_excel('DataSecond_rand_limit_none_extra.xlsx')\n",
    "learning_2nd_theta = pd.read_excel('DataSecond_rand_limit_extra.xlsx')\n",
    "pro_iden = SecondOrderProcessIdentification(loaded_data=learning_2nd)\n",
    "pro_iden_extra = SecondOrderProcessIdentification(n_inputs = 17, loaded_data=learning_2nd_theta)\n",
    "X_train, y_train, X_test, y_test, X_scale_minmax = pro_iden.data_preparation()\n",
    "y_train_TC, y_train_DC = y_train[:,0:1], y_train[:,1:2]\n",
    "y_test_TC, y_test_DC = y_test[:,0:1], y_test[:,1:2]\n",
    "X_train_theta, y_train_theta, X_test_theta, y_test_theta, X_scale_minmax_theta = pro_iden_extra.data_preparation_theta()\n",
    "y_train_TD = y_train_theta[:,2:3]\n",
    "y_test_TD = y_test_theta[:,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\tbatch data accuracy: -3.7637\tbatch data RMSE: 16.484303\n",
      "1\tbatch data accuracy: 0.5985\tbatch data RMSE: 6.905543\n",
      "2\tbatch data accuracy: 0.7307\tbatch data RMSE: 4.382976\n",
      "3\tbatch data accuracy: 0.8823\tbatch data RMSE: 1.760197\n",
      "4\tbatch data accuracy: 0.9598\tbatch data RMSE: 0.735679\n",
      "5\tbatch data accuracy: 0.9797\tbatch data RMSE: 0.426451\n",
      "6\tbatch data accuracy: 0.9895\tbatch data RMSE: 0.311671\n",
      "7\tbatch data accuracy: 0.9788\tbatch data RMSE: 0.284876\n",
      "8\tbatch data accuracy: 0.9891\tbatch data RMSE: 0.210507\n",
      "9\tbatch data accuracy: 0.9929\tbatch data RMSE: 0.195264\n",
      "10\tbatch data accuracy: 0.9912\tbatch data RMSE: 0.166680\n",
      "11\tbatch data accuracy: 0.9939\tbatch data RMSE: 0.160313\n",
      "12\tbatch data accuracy: 0.9959\tbatch data RMSE: 0.141082\n",
      "13\tbatch data accuracy: 0.9926\tbatch data RMSE: 0.112108\n",
      "14\tbatch data accuracy: 0.9928\tbatch data RMSE: 0.150357\n",
      "15\tbatch data accuracy: 0.9939\tbatch data RMSE: 0.153793\n",
      "16\tbatch data accuracy: 0.9839\tbatch data RMSE: 0.134044\n",
      "17\tbatch data accuracy: 0.9816\tbatch data RMSE: 0.114306\n",
      "18\tbatch data accuracy: 0.9905\tbatch data RMSE: 0.120004\n",
      "19\tbatch data accuracy: 0.9911\tbatch data RMSE: 0.078090\n",
      "20\tbatch data accuracy: 0.9931\tbatch data RMSE: 0.130338\n",
      "21\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.088633\n",
      "22\tbatch data accuracy: 0.9951\tbatch data RMSE: 0.113580\n",
      "23\tbatch data accuracy: 0.9971\tbatch data RMSE: 0.115615\n",
      "24\tbatch data accuracy: 0.9855\tbatch data RMSE: 0.124797\n",
      "25\tbatch data accuracy: 0.9936\tbatch data RMSE: 0.106662\n",
      "26\tbatch data accuracy: 0.9819\tbatch data RMSE: 0.106984\n",
      "27\tbatch data accuracy: 0.9976\tbatch data RMSE: 0.080968\n",
      "28\tbatch data accuracy: 0.9945\tbatch data RMSE: 0.181583\n",
      "29\tbatch data accuracy: 0.9928\tbatch data RMSE: 0.118784\n",
      "30\tbatch data accuracy: 0.9921\tbatch data RMSE: 0.106098\n",
      "31\tbatch data accuracy: 0.9962\tbatch data RMSE: 0.109604\n",
      "32\tbatch data accuracy: 0.9944\tbatch data RMSE: 0.083724\n",
      "33\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.090766\n",
      "34\tbatch data accuracy: 0.9924\tbatch data RMSE: 0.086825\n",
      "35\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.079207\n",
      "36\tbatch data accuracy: 0.9945\tbatch data RMSE: 0.102523\n",
      "37\tbatch data accuracy: 0.9943\tbatch data RMSE: 0.113472\n",
      "38\tbatch data accuracy: 0.9962\tbatch data RMSE: 0.082411\n",
      "39\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.081393\n",
      "40\tbatch data accuracy: 0.9943\tbatch data RMSE: 0.115939\n",
      "41\tbatch data accuracy: 0.9963\tbatch data RMSE: 0.081218\n",
      "42\tbatch data accuracy: 0.9953\tbatch data RMSE: 0.074907\n",
      "43\tbatch data accuracy: 0.9949\tbatch data RMSE: 0.078786\n",
      "44\tbatch data accuracy: 0.9945\tbatch data RMSE: 0.087796\n",
      "45\tbatch data accuracy: 0.9922\tbatch data RMSE: 0.080244\n",
      "46\tbatch data accuracy: 0.9955\tbatch data RMSE: 0.107845\n",
      "47\tbatch data accuracy: 0.9961\tbatch data RMSE: 0.084692\n",
      "48\tbatch data accuracy: 0.9950\tbatch data RMSE: 0.127407\n",
      "49\tbatch data accuracy: 0.9917\tbatch data RMSE: 0.096580\n",
      "50\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.094445\n",
      "51\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.073813\n",
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_tau_learning.ckpt\n",
      "\n",
      "\n",
      "tau test data accuracy: 0.9957188\n",
      "\n",
      "\n",
      "predictive time constant:\n",
      " [[33.103027 ]\n",
      " [31.705614 ]\n",
      " [14.994921 ]\n",
      " [17.827814 ]\n",
      " [44.15405  ]\n",
      " [46.80847  ]\n",
      " [15.024824 ]\n",
      " [ 1.1536183]\n",
      " [49.71065  ]\n",
      " [28.72914  ]]\n",
      "real time constant:\n",
      " [[33.09874239]\n",
      " [32.05643939]\n",
      " [14.99974368]\n",
      " [17.7893494 ]\n",
      " [44.21363368]\n",
      " [47.02557004]\n",
      " [15.02484384]\n",
      " [ 1.10767973]\n",
      " [49.68862545]\n",
      " [28.72437279]]\n"
     ]
    }
   ],
   "source": [
    "#Time Constant training\n",
    "pro_iden_tau = SecondOrderProcessIdentification(n_hidden_layers=4, learning_rate = 0.00001, momentum = 0.999,\n",
    "                                               learning_object=\"tau\", random_state=42)\n",
    "pro_iden_tau.fit(X=X_train, y=y_train_TC, n_epochs=52)\n",
    "y_pred_TC = pro_iden_tau.predict(X=X_test, y=y_test_TC)\n",
    "print(\"predictive time constant:\\n\", y_pred_TC)\n",
    "print(\"real time constant:\\n\", y_test_TC[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\tbatch data accuracy: -0.0074\tbatch data RMSE: 0.254656\n",
      "1\tbatch data accuracy: 0.8155\tbatch data RMSE: 0.108929\n",
      "2\tbatch data accuracy: 0.7212\tbatch data RMSE: 0.068533\n",
      "3\tbatch data accuracy: 0.8439\tbatch data RMSE: 0.050013\n",
      "4\tbatch data accuracy: 0.8706\tbatch data RMSE: 0.029560\n",
      "5\tbatch data accuracy: 0.9359\tbatch data RMSE: 0.012998\n",
      "6\tbatch data accuracy: 0.9817\tbatch data RMSE: 0.007043\n",
      "7\tbatch data accuracy: 0.9781\tbatch data RMSE: 0.007589\n",
      "8\tbatch data accuracy: 0.9887\tbatch data RMSE: 0.004264\n",
      "9\tbatch data accuracy: 0.9910\tbatch data RMSE: 0.003412\n",
      "10\tbatch data accuracy: 0.9890\tbatch data RMSE: 0.003363\n",
      "11\tbatch data accuracy: 0.9947\tbatch data RMSE: 0.003166\n",
      "12\tbatch data accuracy: 0.9934\tbatch data RMSE: 0.001926\n",
      "13\tbatch data accuracy: 0.9951\tbatch data RMSE: 0.001848\n",
      "14\tbatch data accuracy: 0.9958\tbatch data RMSE: 0.001686\n",
      "15\tbatch data accuracy: 0.9938\tbatch data RMSE: 0.001385\n",
      "16\tbatch data accuracy: 0.9958\tbatch data RMSE: 0.001171\n",
      "17\tbatch data accuracy: 0.9945\tbatch data RMSE: 0.001124\n",
      "18\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.000983\n",
      "19\tbatch data accuracy: 0.9971\tbatch data RMSE: 0.001129\n",
      "20\tbatch data accuracy: 0.9969\tbatch data RMSE: 0.000888\n",
      "21\tbatch data accuracy: 0.9959\tbatch data RMSE: 0.000968\n",
      "22\tbatch data accuracy: 0.9961\tbatch data RMSE: 0.001186\n",
      "23\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.001286\n",
      "24\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.001011\n",
      "25\tbatch data accuracy: 0.9966\tbatch data RMSE: 0.000859\n",
      "26\tbatch data accuracy: 0.9958\tbatch data RMSE: 0.000811\n",
      "27\tbatch data accuracy: 0.9979\tbatch data RMSE: 0.000749\n",
      "28\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.000698\n",
      "29\tbatch data accuracy: 0.9974\tbatch data RMSE: 0.001054\n",
      "30\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.000787\n",
      "31\tbatch data accuracy: 0.9960\tbatch data RMSE: 0.000918\n",
      "32\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.000906\n",
      "33\tbatch data accuracy: 0.9977\tbatch data RMSE: 0.000669\n",
      "34\tbatch data accuracy: 0.9954\tbatch data RMSE: 0.000751\n",
      "35\tbatch data accuracy: 0.9976\tbatch data RMSE: 0.000656\n",
      "36\tbatch data accuracy: 0.9974\tbatch data RMSE: 0.000748\n",
      "37\tbatch data accuracy: 0.9945\tbatch data RMSE: 0.000820\n",
      "38\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.000631\n",
      "39\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.000774\n",
      "40\tbatch data accuracy: 0.9966\tbatch data RMSE: 0.000645\n",
      "41\tbatch data accuracy: 0.9965\tbatch data RMSE: 0.000763\n",
      "42\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.000841\n",
      "43\tbatch data accuracy: 0.9980\tbatch data RMSE: 0.000644\n",
      "44\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.001017\n",
      "45\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.000821\n",
      "46\tbatch data accuracy: 0.9966\tbatch data RMSE: 0.000834\n",
      "47\tbatch data accuracy: 0.9963\tbatch data RMSE: 0.000789\n",
      "48\tbatch data accuracy: 0.9978\tbatch data RMSE: 0.000696\n",
      "49\tbatch data accuracy: 0.9969\tbatch data RMSE: 0.000693\n",
      "50\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.001037\n",
      "51\tbatch data accuracy: 0.9981\tbatch data RMSE: 0.000970\n",
      "52\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.000896\n",
      "53\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.001007\n",
      "54\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.000799\n",
      "55\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.000811\n",
      "56\tbatch data accuracy: 0.9974\tbatch data RMSE: 0.000799\n",
      "57\tbatch data accuracy: 0.9951\tbatch data RMSE: 0.000809\n",
      "58\tbatch data accuracy: 0.9975\tbatch data RMSE: 0.000663\n",
      "59\tbatch data accuracy: 0.9981\tbatch data RMSE: 0.000879\n",
      "60\tbatch data accuracy: 0.9976\tbatch data RMSE: 0.000723\n",
      "61\tbatch data accuracy: 0.9971\tbatch data RMSE: 0.000677\n",
      "62\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.000729\n",
      "63\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.000804\n",
      "64\tbatch data accuracy: 0.9959\tbatch data RMSE: 0.000724\n",
      "65\tbatch data accuracy: 0.9975\tbatch data RMSE: 0.001164\n",
      "66\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.000655\n",
      "67\tbatch data accuracy: 0.9977\tbatch data RMSE: 0.000637\n",
      "68\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.000711\n",
      "69\tbatch data accuracy: 0.9962\tbatch data RMSE: 0.000763\n",
      "70\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.000626\n",
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_zeta_learning.ckpt\n",
      "\n",
      "\n",
      "zeta test data accuracy: 0.9969132\n",
      "\n",
      "\n",
      "predictive damping coefficient:\n",
      " [[0.29941553]\n",
      " [0.0504384 ]\n",
      " [0.76305485]\n",
      " [0.6872143 ]\n",
      " [0.44334602]\n",
      " [0.24481088]\n",
      " [0.39566922]\n",
      " [0.3081109 ]\n",
      " [0.803856  ]\n",
      " [0.30247045]]\n",
      "real damping coefficient:\n",
      " [[0.29932086]\n",
      " [0.04984519]\n",
      " [0.76262479]\n",
      " [0.68749178]\n",
      " [0.44316585]\n",
      " [0.24458509]\n",
      " [0.39525051]\n",
      " [0.30764656]\n",
      " [0.80407585]\n",
      " [0.30276044]]\n"
     ]
    }
   ],
   "source": [
    "#Damping Coefficient training\n",
    "pro_iden_zeta = SecondOrderProcessIdentification(n_hidden_layers=5, learning_rate = 0.00005,\n",
    "                                                 momentum = 0.999, n_neurons = 140, batch_size = 150,\n",
    "                                                 learning_object=\"zeta\", random_state=42)\n",
    "pro_iden_zeta.fit(X=X_train, y=y_train_DC, n_epochs=71)\n",
    "y_pred_DC = pro_iden_zeta.predict(X=X_test, y=y_test_DC)\n",
    "print(\"predictive damping coefficient:\\n\", y_pred_DC)\n",
    "print(\"real damping coefficient:\\n\", y_test_DC[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\tbatch data accuracy: 0.9254\tbatch data RMSE: 0.643157\n",
      "1\tbatch data accuracy: 0.9669\tbatch data RMSE: 0.073763\n",
      "2\tbatch data accuracy: 0.9958\tbatch data RMSE: 0.062490\n",
      "3\tbatch data accuracy: 0.9918\tbatch data RMSE: 0.066738\n",
      "4\tbatch data accuracy: 0.9908\tbatch data RMSE: 0.095186\n",
      "5\tbatch data accuracy: 0.9788\tbatch data RMSE: 0.044048\n",
      "6\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.022915\n",
      "7\tbatch data accuracy: 0.9949\tbatch data RMSE: 0.042715\n",
      "8\tbatch data accuracy: 0.9836\tbatch data RMSE: 0.045917\n",
      "9\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.037510\n",
      "10\tbatch data accuracy: 0.9884\tbatch data RMSE: 0.059425\n",
      "11\tbatch data accuracy: 0.9956\tbatch data RMSE: 0.045859\n",
      "12\tbatch data accuracy: 0.9891\tbatch data RMSE: 0.033588\n",
      "13\tbatch data accuracy: 0.9916\tbatch data RMSE: 0.041843\n",
      "14\tbatch data accuracy: 0.9862\tbatch data RMSE: 0.052481\n",
      "15\tbatch data accuracy: 0.9776\tbatch data RMSE: 0.018025\n",
      "16\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.018355\n",
      "17\tbatch data accuracy: 0.9685\tbatch data RMSE: 0.042457\n",
      "18\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.034700\n",
      "19\tbatch data accuracy: 0.9909\tbatch data RMSE: 0.040266\n",
      "20\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.020581\n",
      "21\tbatch data accuracy: 0.9979\tbatch data RMSE: 0.037186\n",
      "22\tbatch data accuracy: 0.9953\tbatch data RMSE: 0.025314\n",
      "23\tbatch data accuracy: 0.9977\tbatch data RMSE: 0.029951\n",
      "24\tbatch data accuracy: 0.9901\tbatch data RMSE: 0.017744\n",
      "25\tbatch data accuracy: 0.9947\tbatch data RMSE: 0.038849\n",
      "26\tbatch data accuracy: 0.9934\tbatch data RMSE: 0.028087\n",
      "27\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.020697\n",
      "28\tbatch data accuracy: 0.9982\tbatch data RMSE: 0.014889\n",
      "29\tbatch data accuracy: 0.9954\tbatch data RMSE: 0.038411\n",
      "30\tbatch data accuracy: 0.9936\tbatch data RMSE: 0.027426\n",
      "31\tbatch data accuracy: 0.9950\tbatch data RMSE: 0.019324\n",
      "32\tbatch data accuracy: 0.9960\tbatch data RMSE: 0.028242\n",
      "33\tbatch data accuracy: 0.9979\tbatch data RMSE: 0.026513\n",
      "34\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.018256\n",
      "35\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.018460\n",
      "36\tbatch data accuracy: 0.9976\tbatch data RMSE: 0.019811\n",
      "37\tbatch data accuracy: 0.9978\tbatch data RMSE: 0.019166\n",
      "38\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.022307\n",
      "39\tbatch data accuracy: 0.9980\tbatch data RMSE: 0.022422\n",
      "40\tbatch data accuracy: 0.9497\tbatch data RMSE: 0.028139\n",
      "41\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.031298\n",
      "42\tbatch data accuracy: 0.9919\tbatch data RMSE: 0.015502\n",
      "43\tbatch data accuracy: 0.9815\tbatch data RMSE: 0.022103\n",
      "44\tbatch data accuracy: 0.9986\tbatch data RMSE: 0.018525\n",
      "45\tbatch data accuracy: 0.9969\tbatch data RMSE: 0.027447\n",
      "46\tbatch data accuracy: 0.9960\tbatch data RMSE: 0.025600\n",
      "47\tbatch data accuracy: 0.9851\tbatch data RMSE: 0.019913\n",
      "48\tbatch data accuracy: 0.9920\tbatch data RMSE: 0.011825\n",
      "49\tbatch data accuracy: 0.9918\tbatch data RMSE: 0.024196\n",
      "50\tbatch data accuracy: 0.9990\tbatch data RMSE: 0.019129\n",
      "51\tbatch data accuracy: 0.9987\tbatch data RMSE: 0.012529\n",
      "52\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.020512\n",
      "53\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.019355\n",
      "54\tbatch data accuracy: 0.9982\tbatch data RMSE: 0.022121\n",
      "55\tbatch data accuracy: 0.9982\tbatch data RMSE: 0.026083\n",
      "56\tbatch data accuracy: 0.9956\tbatch data RMSE: 0.013266\n",
      "57\tbatch data accuracy: 0.9957\tbatch data RMSE: 0.040661\n",
      "58\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.019299\n",
      "59\tbatch data accuracy: 0.9953\tbatch data RMSE: 0.015607\n",
      "60\tbatch data accuracy: 0.9980\tbatch data RMSE: 0.019126\n",
      "61\tbatch data accuracy: 0.9979\tbatch data RMSE: 0.007889\n",
      "62\tbatch data accuracy: 0.9988\tbatch data RMSE: 0.021999\n",
      "63\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.018257\n",
      "64\tbatch data accuracy: 0.9769\tbatch data RMSE: 0.012823\n",
      "65\tbatch data accuracy: 0.9968\tbatch data RMSE: 0.031041\n",
      "66\tbatch data accuracy: 0.9953\tbatch data RMSE: 0.014418\n",
      "67\tbatch data accuracy: 0.9927\tbatch data RMSE: 0.026538\n",
      "68\tbatch data accuracy: 0.9924\tbatch data RMSE: 0.030997\n",
      "69\tbatch data accuracy: 0.9989\tbatch data RMSE: 0.016189\n",
      "70\tbatch data accuracy: 0.9980\tbatch data RMSE: 0.008447\n",
      "71\tbatch data accuracy: 0.9978\tbatch data RMSE: 0.015438\n",
      "72\tbatch data accuracy: 0.9860\tbatch data RMSE: 0.033439\n",
      "73\tbatch data accuracy: 0.9957\tbatch data RMSE: 0.014328\n",
      "74\tbatch data accuracy: 0.9977\tbatch data RMSE: 0.020108\n",
      "75\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.010174\n",
      "76\tbatch data accuracy: 0.8539\tbatch data RMSE: 0.028628\n",
      "77\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.032532\n",
      "78\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.017809\n",
      "79\tbatch data accuracy: 0.9909\tbatch data RMSE: 0.023767\n",
      "80\tbatch data accuracy: 0.9985\tbatch data RMSE: 0.012778\n",
      "81\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.017953\n",
      "82\tbatch data accuracy: 0.9971\tbatch data RMSE: 0.015095\n",
      "83\tbatch data accuracy: 0.9937\tbatch data RMSE: 0.020153\n",
      "84\tbatch data accuracy: 0.9987\tbatch data RMSE: 0.018229\n",
      "85\tbatch data accuracy: 0.9800\tbatch data RMSE: 0.019401\n",
      "86\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.032513\n",
      "87\tbatch data accuracy: 0.9986\tbatch data RMSE: 0.008283\n",
      "88\tbatch data accuracy: 0.9964\tbatch data RMSE: 0.027154\n",
      "89\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.037005\n",
      "90\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.016226\n",
      "91\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.020967\n",
      "92\tbatch data accuracy: 0.9973\tbatch data RMSE: 0.022743\n",
      "93\tbatch data accuracy: 0.9990\tbatch data RMSE: 0.009159\n",
      "94\tbatch data accuracy: 0.9983\tbatch data RMSE: 0.016347\n",
      "95\tbatch data accuracy: 0.9981\tbatch data RMSE: 0.019781\n",
      "96\tbatch data accuracy: 0.9977\tbatch data RMSE: 0.013340\n",
      "97\tbatch data accuracy: 0.9938\tbatch data RMSE: 0.038896\n",
      "98\tbatch data accuracy: 0.9972\tbatch data RMSE: 0.010156\n",
      "99\tbatch data accuracy: 0.9974\tbatch data RMSE: 0.019696\n",
      "100\tbatch data accuracy: 0.9986\tbatch data RMSE: 0.013711\n",
      "101\tbatch data accuracy: 0.9965\tbatch data RMSE: 0.021061\n",
      "102\tbatch data accuracy: 0.9963\tbatch data RMSE: 0.026216\n",
      "103\tbatch data accuracy: 0.9963\tbatch data RMSE: 0.024357\n",
      "104\tbatch data accuracy: 0.9984\tbatch data RMSE: 0.031539\n",
      "105\tbatch data accuracy: 0.9917\tbatch data RMSE: 0.018888\n",
      "106\tbatch data accuracy: 0.9985\tbatch data RMSE: 0.024288\n",
      "107\tbatch data accuracy: 0.9974\tbatch data RMSE: 0.018913\n",
      "108\tbatch data accuracy: 0.9970\tbatch data RMSE: 0.019661\n",
      "109\tbatch data accuracy: 0.9967\tbatch data RMSE: 0.013402\n",
      "110\tbatch data accuracy: 0.9985\tbatch data RMSE: 0.018151\n",
      "111\tbatch data accuracy: 0.9987\tbatch data RMSE: 0.006894\n",
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_theta_learning.ckpt\n",
      "\n",
      "\n",
      "theta test data accuracy: 0.994386\n",
      "\n",
      "\n",
      "predictive time delay:\n",
      " [[11.265226 ]\n",
      " [ 6.2419415]\n",
      " [ 5.8383827]\n",
      " [12.87438  ]\n",
      " [11.228582 ]\n",
      " [ 4.4546328]\n",
      " [ 3.185939 ]\n",
      " [14.815966 ]\n",
      " [ 9.698584 ]\n",
      " [18.187204 ]]\n",
      "real time delay:\n",
      " [[11.27321627]\n",
      " [ 6.23872235]\n",
      " [ 5.84386544]\n",
      " [12.87467499]\n",
      " [11.25366868]\n",
      " [ 4.44002479]\n",
      " [ 3.16720515]\n",
      " [14.84161673]\n",
      " [ 9.70987571]\n",
      " [18.19042404]]\n"
     ]
    }
   ],
   "source": [
    "#Time Delay training\n",
    "pro_iden_theta = SecondOrderProcessIdentification(n_inputs=17, n_hidden_layers=4, learning_rate = 0.00001,\n",
    "                                                  momentum = 0.999, n_neurons = 140, batch_size = 10,\n",
    "                                                  learning_object=\"theta\", random_state=42)\n",
    "pro_iden_theta.fit(X=X_train_theta, y=y_train_TD, n_epochs=112)\n",
    "y_pred_TD = pro_iden_theta.predict(X=X_test_theta, y=y_test_TD)\n",
    "print(\"predictive time delay:\\n\", y_pred_TD)\n",
    "print(\"real time delay:\\n\", y_test_TD[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data load for modeling\n",
    "# The data used here is just data to work. Instead, change to the data you want to model.\n",
    "operating_2nd = pd.read_excel('DataSecond_Operating_rand_limit_none_extra.xlsx')\n",
    "operating_2nd_extra = pd.read_excel('DataSecond_Operating_rand_limit_extra.xlsx')\n",
    "pro_iden = SecondOrderProcessIdentification(loaded_data = operating_2nd)\n",
    "pro_iden_extra = SecondOrderProcessIdentification(n_inputs=17, loaded_data = operating_2nd_extra)\n",
    "X_op, y_op, process_gain = pro_iden.operating_data_preparation(X_scale_minmax=X_scale_minmax)\n",
    "y_op_TC, y_op_DC = y_op[:,0:1], y_op[:,1:2]\n",
    "X_op_theta, y_op_theta = pro_iden_extra.operating_data_preparation_theta(X_scale_minmax=X_scale_minmax_theta)\n",
    "y_op_TD = y_op_theta[:,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_tau_learning.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_zeta_learning.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./2nd_order_process_theta_learning.ckpt\n",
      "\n",
      "\n",
      "predictive time constant: [[33.17033]]\n",
      "\n",
      "\n",
      "predictive damping coefficient: [[0.09429336]]\n",
      "\n",
      "\n",
      "predictive time delay: [[10.095656]]\n"
     ]
    }
   ],
   "source": [
    "tau_pred = pro_iden_tau.operating_predict(X=X_op, y=y_op_TC)\n",
    "zeta_pred = pro_iden_zeta.operating_predict(X=X_op, y=y_op_DC)\n",
    "theta_pred = pro_iden_theta.operating_predict(X=X_op_theta, y=y_op_TD)\n",
    "\n",
    "print(\"\\n\"); print(\"predictive time constant:\", tau_pred)\n",
    "print(\"\\n\"); print(\"predictive damping coefficient:\", zeta_pred)\n",
    "print(\"\\n\"); print(\"predictive time delay:\", theta_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PID Controller by IMC Tuning_PIPC: Kc = 16.745881, tau_i = 38.218159, tau_d = 4.381114\n",
      "\n",
      "\n",
      "PID Controller by IMC Tuning_PDCT: Kc = 0.010757, tau_i = 6.255484, tau_d = 1.563871\n",
      "\n",
      "\n",
      "PID Controller by ITAE-2-setpoint tuning: Kc = 0.009705, tau_i = 6.496050, tau_d = 163.562698\n",
      "PID Controller by ITAE-2-disturbance tuning: Kc = 0.106017, tau_i = 29.576002, tau_d = 27.496960\n"
     ]
    }
   ],
   "source": [
    "PID_tuning = PIDTuning(tau=tau_pred, zeta=zeta_pred, theta=theta_pred, PID_type=\"PID\")\n",
    "process_gain = process_gain[:1]\n",
    "\n",
    "PID_tuning.IMC_tuning_PIPC(process_gain=process_gain)\n",
    "print(\"\\n\")\n",
    "PID_tuning.IMC_tuning_PDCT(process_gain=process_gain, Condition=\"default value\")\n",
    "print(\"\\n\")\n",
    "PID_tuning.ITAE_2_tuning(process_gain=process_gain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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