diff --git a/README.md b/README.md
index ff6dea2c..682ed5bf 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# AAE497 Fall 2019 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jgoppert/aae497-f19/master) [](https://mybinder.org/v2/gh/jgoppert/aae497-f19/master?urlpath=lab) [![nbviewer](https://img.shields.io/badge/view%20on-nbviewer-brightgreen.svg)](http://nbviewer.jupyter.org/github/jgoppert/aae497-f19/tree/master)
+# AAE497 Fall 2019 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/cohen39/aae497-f19/master) [](https://mybinder.org/v2/gh/cohen39/aae497-f19/master?urlpath=lab) [![nbviewer](https://img.shields.io/badge/view%20on-nbviewer-brightgreen.svg)](http://nbviewer.jupyter.org/github/cohen39/aae497-f19/tree/master)
Class notebook.
diff --git a/homework/1-F16-Pitch.ipynb b/homework/1-F16-Pitch.ipynb
deleted file mode 100644
index 3c4f1386..00000000
--- a/homework/1-F16-Pitch.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.insert(0, '../python/casadi_f16')\n",
- "import f16\n",
- "import control\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from analysis import loop_analysis"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Pitch Autopilot Design\n",
- "\n",
- "* See Pitch Autopilot design example in section 4.6 of Stevens and Lewis.\n",
- "* Homework 1: Due 8/30 @ 11 pm: Trim the F16 model around a VT=550 ft/s, 20 deg/s yaw rate turn.\n",
- "* Find the A, B, C, D matrices for the state space model.\n",
- "* Find the transfer function for the aileron to pitch rate (Q).\n",
- "* Design a PID controller attempting to meet the following specifications\n",
- " * Maximum overshoot: 20%\n",
- " * Rise time: 0.1 second\n",
- " * Settling time 1 second\n",
- "* Simulate and plot the response of your controlled system and the linear model for a step response in pitch rate of 10 deg/s and 100 deg/s. How do the nonlinear and linear responses compare?\n",
- "* Using git, fork aae497-f16 on github. Complete the homework. Submit your homework via pull request on aae497-f16."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 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": "3.7.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/homework/6-Casadi-Brach.ipynb b/homework/6-Casadi-Brach.ipynb
index 4b3f21af..48479cf6 100644
--- a/homework/6-Casadi-Brach.ipynb
+++ b/homework/6-Casadi-Brach.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@@ -34,24 +34,49 @@
" starts at a height of 1 m and ends at a height of 0 m and the length of the path is\n",
" x_end m.\n",
" \"\"\"\n",
- " x = np.linspace(0, x_end, n_x) # x position where path changes\n",
- " dx = x[1] - x[0] # path steps width\n",
+ " x_pos = np.linspace(0, x_end, n_x) # x position where path changes\n",
" n_dy = n_x - 1 # number of height changes we need to find\n",
- " dy0 = -(1/n_dy)*np.ones(n_dy) # initial guess for height change along path\n",
+ " dy_pos_guess = -(1/n_dy)*np.ones(n_dy) # initial guess for height change along path\n",
" \n",
- " for i in range(n_dy):\n",
- " # find average velocit of this track segment\n",
- " # find length of this track segment\n",
- " # use the above to find the d\n",
+ " x_1 = x_pos[0]\n",
+ " x_2 = x_pos[-1]\n",
+ " y_1 = 1\n",
+ " y_2 = 0\n",
+ " \n",
+ " dy_pos = ca.SX.sym('y_pos',n_x-1)\n",
+ " nlp = {'x': dy_pos, 'f': t12(x_pos,dy_pos), 'g': y_1+ca.sum1(dy_pos)}#Vary dy_pos to minimiz OF 'f' constrained by the sum of dy's = -1\n",
+ " tmin = ca.nlpsol('tmin', 'ipopt', nlp, {\n",
+ " 'print_time': 0,\n",
+ " 'ipopt': {\n",
+ " 'sb': 'yes',\n",
+ " 'print_level': 0,\n",
+ " }\n",
+ " })\n",
+ " res = tmin(x0=dy_pos_guess,lbg=y_2,ubg=y_2) \n",
+ " dy_opt = res['x']\n",
+ " y_opt = np.concatenate((np.array([y_1]),y_1+np.cumsum(dy_opt)),axis=0)\n",
+ " return x_pos, y_opt\n",
+ "\n",
+ "\n",
+ "def t12(x_pos,dy_pos):\n",
+ " g = 9.81\n",
+ " y = 1 #set initial height\n",
+ " t_total = 0\n",
+ " dx = np.mean(np.diff(x_pos))\n",
+ " \n",
+ " for i in range(len(x_pos)-1):\n",
+ " dy = dy_pos[i]\n",
+ " length = ca.sqrt(ca.power(dy,2)+ca.power(dx,2))\n",
+ " dt = 2*length/(ca.sqrt(2*g*(1-y))+ca.sqrt(2*g*(1-y-dy)))\n",
+ " t_total = t_total + dt\n",
+ " y = y + dy\n",
" \n",
- " dy_opt = dy0 # TODO, find optimal change in y along path\n",
- " y_opt = ca.vertcat(1, 1 + np.cumsum(dy_opt))\n",
- " return x, y_opt"
+ " return t_total"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
@@ -81,22 +106,22 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 4,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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