387 lines
135 KiB
Plaintext
387 lines
135 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from Regler_class_file import P_controller_class\n",
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"from Regler_class_file import PI_controller_class\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # %matplotlib widget\n",
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"# pi_controller = PI_controller_class(setpoint=100,proportionality_constant=1,Ti = 30,timestep = 0.1)\n",
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"\n",
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"# t_max = 100 #s\n",
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"# dt = 0.1 #s\n",
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"# nt = int(t_max//dt)\n",
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"# t_vec = np.arange(0,nt+1,1)*dt\n",
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"\n",
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"# PV_0 = 50\n",
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"\n",
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"# PV_vec = np.full_like(t_vec,PV_0)\n",
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"\n",
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"# for i in range(1,nt+1):\n",
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"# pi_controller.calculate_error(PV_vec[i-1])\n",
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"\n",
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"# if i == 500:\n",
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"# pi_controller.SP = 0.\n",
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"# PV_vec[i] = PV_vec[i-1]+pi_controller.get_control_variable()\n",
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"\n",
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"\n",
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"\n",
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"# plt.plot(t_vec,PV_vec,'.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"SP_0 = 1.1\n",
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"SP_1 = 0.24\n",
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"PV_0 = 0.5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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}
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],
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"source": [
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"n = 10\n",
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"Kp_max = 5.\n",
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"Ti_max = 5.\n",
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"d_Kp = Kp_max/n\n",
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"d_Ti = Ti_max/n\n",
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"kp_vec = np.arange(1,n+1,1)*d_Kp\n",
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"Ti_vec = np.arange(1,n+1,1)*d_Ti\n",
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"\n",
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"XX,YY = np.meshgrid(kp_vec,Ti_vec)\n",
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"\n",
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"ise_mat = np.empty_like(XX)\n",
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"iae_mat = np.empty_like(XX)\n",
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"itse_mat = np.empty_like(XX)\n",
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"itae_mat = np.empty_like(XX)\n",
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"\n",
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"\n",
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"t_max = 100 #s\n",
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"dt = 0.05 #s\n",
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"nt = int(t_max//dt)\n",
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"t_vec = np.arange(0,nt+1,1)*dt\n",
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"\n",
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"for i in range(n):\n",
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" if i%10 == 0:\n",
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" print(i)\n",
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" for j in range(n):\n",
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" Kp = XX[i,j]\n",
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" Ti = YY[i,j]\n",
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"\n",
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" c = PI_controller_class(SP_0,Kp,Ti,dt)\n",
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"\n",
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" PV_vec = np.full_like(t_vec,PV_0)\n",
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"\n",
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" for t in range(1,nt+1):\n",
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" c.calculate_error(PV_vec[t-1])\n",
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"\n",
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" if t == 500:\n",
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" c.SP = SP_1\n",
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" PV_vec[t] = PV_vec[t-1]+c.get_control_variable()\n",
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" \n",
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" ise_mat[i,j],iae_mat[i,j],itse_mat[i,j],itae_mat[i,j] = np.log(c.get_performance_indicators())\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.colorbar.Colorbar at 0x162b29d6880>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d9226014321c46daa6791bc1dfd0f06d",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": 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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" </div>\n",
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],
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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},
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{
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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" Figure\n",
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]
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},
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"metadata": {},
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},
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{
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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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' width=640.0/>\n",
|
|
" </div>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib widget\n",
|
|
"fig1,axs1 = plt.subplots(1,1)\n",
|
|
"ise_min = np.min(ise_mat)\n",
|
|
"ise_max = np.max(ise_mat)\n",
|
|
"mesh1 = axs1.pcolormesh(XX,YY,ise_mat,cmap='RdBu', vmin=ise_min, vmax=ise_max,shading='nearest')\n",
|
|
"fig1.colorbar(mesh1)\n",
|
|
"fig2,axs2 = plt.subplots(1,1)\n",
|
|
"iae_min = np.min(iae_mat)\n",
|
|
"iae_max = np.max(iae_mat)\n",
|
|
"mesh2 = axs2.pcolormesh(XX,YY,iae_mat,cmap='RdBu', vmin=iae_min, vmax=iae_max,shading='nearest')\n",
|
|
"fig2.colorbar(mesh2)\n",
|
|
"fig3,axs3 = plt.subplots(1,1)\n",
|
|
"itse_min = np.min(itse_mat)\n",
|
|
"itse_max = np.max(itse_mat)\n",
|
|
"mesh3 = axs3.pcolormesh(XX,YY,itse_mat,cmap='RdBu', vmin=itse_min, vmax=itse_max,shading='nearest')\n",
|
|
"fig3.colorbar(mesh3)\n",
|
|
"fig4,axs4 = plt.subplots(1,1)\n",
|
|
"itae_min = np.min(itae_mat)\n",
|
|
"itae_max = np.max(itae_mat)\n",
|
|
"mesh4 = axs4.pcolormesh(XX,YY,itae_mat,cmap='RdBu', vmin=itae_min, vmax=itae_max,shading='nearest')\n",
|
|
"fig4.colorbar(mesh4)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ise_ind = np.unravel_index(np.argmin(ise_mat,axis=None),ise_mat.shape)\n",
|
|
"Kp_ise = XX[ise_ind]\n",
|
|
"Ti_ise = YY[ise_ind]\n",
|
|
"iae_ind = np.unravel_index(np.argmin(iae_mat,axis=None),iae_mat.shape)\n",
|
|
"Kp_iae = XX[iae_ind]\n",
|
|
"Ti_iae = YY[iae_ind]\n",
|
|
"itse_ind = np.unravel_index(np.argmin(itse_mat,axis=None),itse_mat.shape)\n",
|
|
"Kp_itse = XX[itse_ind]\n",
|
|
"Ti_itse = YY[itse_ind]\n",
|
|
"itae_ind = np.unravel_index(np.argmin(itae_mat,axis=None),itae_mat.shape)\n",
|
|
"Kp_itae = XX[itae_ind]\n",
|
|
"Ti_itae = YY[itae_ind]\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x162b38c74f0>]"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "05f5663bd9ab4b23a30ec4ee94e41a1e",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
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"image/png": 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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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' width=640.0/>\n",
|
|
" </div>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"t_max = 100 #s\n",
|
|
"dt = 0.1 #s\n",
|
|
"nt = int(t_max//dt)\n",
|
|
"t_vec = np.arange(0,nt+1,1)*dt\n",
|
|
"\n",
|
|
"\n",
|
|
"PV_vec_ise = np.full_like(t_vec,PV_0)\n",
|
|
"PV_vec_iae = np.full_like(t_vec,PV_0)\n",
|
|
"PV_vec_itse = np.full_like(t_vec,PV_0)\n",
|
|
"PV_vec_itae = np.full_like(t_vec,PV_0)\n",
|
|
"\n",
|
|
"c_ise = PI_controller_class(SP_0,Kp_ise,Ti_ise,timestep = 0.1)\n",
|
|
"c_iae = PI_controller_class(SP_0,Kp_iae,Ti_iae,timestep = 0.1)\n",
|
|
"c_itse = PI_controller_class(SP_0,Kp_itse,Ti_itse,timestep = 0.1)\n",
|
|
"c_itae = PI_controller_class(SP_0,Kp_itae,Ti_itae,timestep = 0.1)\n",
|
|
"\n",
|
|
"\n",
|
|
"for i in range(1,nt+1):\n",
|
|
" c_ise.calculate_error(PV_vec_ise[i-1])\n",
|
|
" c_iae.calculate_error(PV_vec_iae[i-1])\n",
|
|
" c_itse.calculate_error(PV_vec_itse[i-1])\n",
|
|
" c_itae.calculate_error(PV_vec_itae[i-1])\n",
|
|
"\n",
|
|
" if i == 500:\n",
|
|
" c_ise.SP = SP_1\n",
|
|
" c_iae.SP = SP_1\n",
|
|
" c_itse.SP = SP_1\n",
|
|
" c_itae.SP = SP_1\n",
|
|
"\n",
|
|
" PV_vec_ise[i] = PV_vec_ise[i-1]+c_ise.get_control_variable()\n",
|
|
" PV_vec_iae[i] = PV_vec_iae[i-1]+c_iae.get_control_variable()\n",
|
|
" PV_vec_itse[i] = PV_vec_itse[i-1]+c_itse.get_control_variable()\n",
|
|
" PV_vec_itae[i] = PV_vec_itae[i-1]+c_itae.get_control_variable()\n",
|
|
"\n",
|
|
"fig5 = plt.figure()\n",
|
|
"plt.plot(t_vec,PV_vec_ise,label='ise')\n",
|
|
"plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n",
|
|
"plt.plot(t_vec,PV_vec_itse,'+',label='itse')\n",
|
|
"plt.plot(t_vec,PV_vec_itae,'*',label='itae')"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.13"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
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"interpreter": {
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