149 lines
35 KiB
Plaintext
149 lines
35 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from Regler_class_file import P_controller_class"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"controller = P_controller_class(setpoint=1,proportionality_constant=0.1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"PV_0 = 0.5\n",
|
|
"\n",
|
|
"PV_vec = np.full_like(t_vec,PV_0)\n",
|
|
"controller.calculate_error(PV_vec[0])\n",
|
|
"controller.calculate_error(PV_vec[0])\n",
|
|
"\n",
|
|
"\n",
|
|
"for i in range(2,nt+1):\n",
|
|
" controller.calculate_error(PV_vec[i-1])\n",
|
|
"\n",
|
|
" if i == 100:\n",
|
|
" controller.SP = 0.\n",
|
|
" PV_vec[i] = PV_vec[i-1]+controller.get_control_variable()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x15705d3baf0>]"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "befd47eba60b46139eda036a958f6ca7",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"image/png": "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",
|
|
"text/html": [
|
|
"\n",
|
|
" <div style=\"display: inline-block;\">\n",
|
|
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
|
|
" Figure\n",
|
|
" </div>\n",
|
|
" <img src='data:image/png;base64,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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",
|
|
"\n",
|
|
"plt.plot(t_vec,PV_vec,'+')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"-0.1\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(controller.lower_limit)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.13 ('DT_Slot_3')",
|
|
"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": {
|
|
"interpreter": {
|
|
"hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|