111 lines
30 KiB
Plaintext
111 lines
30 KiB
Plaintext
{
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"cells": [
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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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"from sympy import Symbol, sin, atan, exp\n",
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"from sympy.plotting import plot\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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def pacejka96(b, x, Fz, Sv, Sh):\n",
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" C = b[0]\n",
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" D = (b[1] * Fz + b[2]) * Fz\n",
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" B=((b[3]*Fz^2+b[4]*Fz)*exp(-b[5]*Fz))/(C*D)\n",
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" E = -(1 - (b[6] * Fz^2 +b[7] * Fz + b[8]))\n",
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" Sh=b[9]*Fz+b[10]\n",
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" Sv=0\n",
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" \n",
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" X = x + Sh\n",
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" y = D * sin(C * atan(B * X - E * ( B * X - atan(B * X))))\n",
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" Y = y + Sv\n",
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" return Y"
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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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"source": [
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"# Pacejka coefficients\n",
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"b = [\n",
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" 1.6,\n",
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" 0,\n",
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" 1688,\n",
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" 0,\n",
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" 229,\n",
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" 0,\n",
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" 0,\n",
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" 0,\n",
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" -10,\n",
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" 0,\n",
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" 0\n",
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"]"
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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": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<sympy.plotting.backends.matplotlibbackend.matplotlib.MatplotlibBackend at 0x7f390d53e660>"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"slip_ratio = Symbol('slip_ratio')\n",
|
|
"slip_angle = Symbol('slip_angle')\n",
|
|
"\n",
|
|
"plot(pacejka96(b, slip_ratio, 2, 0, 0) / 2000, (slip_ratio, -100, 100))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"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": "3.13.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|