{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LQ-optimal regulator design for F16 lateral-directional dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a continuous-time linear state-space model for the system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 5.3-1 from [Stevens & Lewis, 3rd ed., 2015](https://www.wiley.com/en-us/Aircraft+Control+and+Simulation%3A+Dynamics%2C+Controls+Design%2C+and+Autonomous+Systems%2C+3rd+Edition-p-9781118870983), page 407. Linear model of lateral-directional dynamics of F16 trimmed at: VT=502ft/s, 302psf dynamic pressure, cg @ 0.35cbar. Includes dynamics of ailerons and rudders and washout filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=f16_lqr.png width=800px>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Symbol        | Name           | Units  |\n",
    "|:--------------|:---------------|:------:|\n",
    "| β             | sideslip angle |  rad   |\n",
    "| ϕ             | bank angle     |  rad   |\n",
    "| p             | roll rate      |  rad/s |\n",
    "| r             | yaw rate       |  rad/s |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [-0.3220  0.0640   0.0364    -0.9917    0.0003     0.0008   0;\n",
    "    0         0        1         0.0037     0          0        0;\n",
    "    -30.6492  0        -3.6784   0.6646     -0.7333    0.1315   0;\n",
    "    8.5396    0        -0.0254   -0.4764    -0.0319    -0.062   0;\n",
    "    0         0        0         0          -20.2      0        0;\n",
    "    0         0        0         0          0          -20.2    0;\n",
    "    0         0        0         57.2958    0          0       -1];\n",
    "\n",
    "B = [0     0;\n",
    "    0      0;\n",
    "    0      0;\n",
    "    0      0;\n",
    "    20.2   0;\n",
    "    0      20.2;\n",
    "    0      0];\n",
    "\n",
    "C = [0      0       0       57.2958 0      0    -1;\n",
    "    0       0       57.2958 0       0      0     0;\n",
    "    57.2958 0       0       0       0      0     0;\n",
    "    0       57.2958 0       0       0      0     0];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `C` matrix converts from radians to degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using ControlSystems, LinearAlgebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = ss(A,B,C,0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion of the model to discrete time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9226667967276305 0.0061993687563318516 … 0.0002121726335909019 0.0; -0.1324663143618211 0.9997066454002713 … 0.00031188493568957813 0.0; … ; 0.0 0.0 … 0.13265546508012172 0.0; 2.292290658677099 0.004976965360488232 … -0.009375212477843127 0.9048374180359594], [2.821613795894789e-5 0.00018468401795193422; -0.001449606139792144 0.0002532096035015619; … ; 0.0 0.8673445349198781; -0.0037522412534400328 -0.007373986216857328], [0.0 0.0 … 0.0 -1.0; 0.0 0.0 … 0.0 0.0; 57.2958 0.0 … 0.0 0.0; 0.0 57.2958 … 0.0 0.0], [0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ts = 0.1;                     # sampling period\n",
    "Gd,x0map = c2d(G,Ts);         # discretized system\n",
    "A,B,C,D = Gd.A,Gd.B,Gd.C,Gd.D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the weights for the LQ controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_beta = 100.0;\n",
    "q_phi = 100;\n",
    "q_p = 1;\n",
    "q_r = 1;\n",
    "q_rw = 1;\n",
    "\n",
    "r_a = 1;\n",
    "r_r = 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = [q_beta, q_phi, q_p, q_r, 0, 0, q_rw];\n",
    "r = [r_a, r_r];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7×7 Array{Float64,2}:\n",
       " 100.0    0.0  0.0  0.0  0.0  0.0  0.0\n",
       "   0.0  100.0  0.0  0.0  0.0  0.0  0.0\n",
       "   0.0    0.0  1.0  0.0  0.0  0.0  0.0\n",
       "   0.0    0.0  0.0  1.0  0.0  0.0  0.0\n",
       "   0.0    0.0  0.0  0.0  0.0  0.0  0.0\n",
       "   0.0    0.0  0.0  0.0  0.0  0.0  0.0\n",
       "   0.0    0.0  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q = diagm(0=>q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Array{Int64,2}:\n",
       " 1  0\n",
       " 0  1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = diagm(0=>r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking controllability and observability conditions for existence of a unique stabilizing solution to ARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the controllability and observability condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank(ctrb(A,B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank(obsv(A,Q))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the discrete-time ARE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7×7 Array{Float64,2}:\n",
       " 22917.0      -709.469      -79.0765     …  -3.10458     198.503    \n",
       "  -709.469     778.937      130.822          0.170749      0.0897329\n",
       "   -79.0765    130.822       30.1433         0.0581277     0.776192 \n",
       "   551.178     223.96        44.4488        -4.32512      33.617    \n",
       "     2.12913    -4.72131     -1.0744         0.00463419   -0.0687775\n",
       "    -3.10458     0.170749     0.0581277  …   0.0133334    -0.0845118\n",
       "   198.503       0.0897329    0.776192      -0.0845118     5.27494  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = dare(A,B,Q,R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forming a feedback gain matrix based on the solution to ARE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×7 Array{Float64,2}:\n",
       "  3.83005  -8.71416   -2.04247   -7.63013  0.0834495   0.00929287  -0.119255\n",
       " -9.79641   0.392317   0.126108  -8.26172  0.00832575  0.0260754   -0.157085"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = (R+B'*S*B)\\B'*S*A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could use the higher-level `lqr` or `dlqr` functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K = lqr(Gd,Q,R)\n",
    "#K = dlqr(A,B,Q,R) # note that here A and B correspond to the discrete-time model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating the response to nonzero initial conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u (generic function with 1 method)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u(x,t) = -K*x # Control law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Array{Float64,1}:\n",
       " 0.1\n",
       " 0.0\n",
       " 0.0\n",
       " 0.0\n",
       " 0.0\n",
       " 0.0\n",
       " 0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=0:Ts:10.0\n",
    "x0 = [1/10, 0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0 0.0 5.72958 0.0; 4.267905268917968 -12.90044103629656 5.296240221889117 -0.7129528775280597; … ; -0.0034802880589172285 -0.005099672191666195 0.003376010636662276 0.00813730111730901; 0.0005185258900812493 -0.012254500541743095 0.0037606824792750233 0.007251190956686843], 0.0:0.1:10.0, [0.1 0.0 … 0.0 0.0; 0.09243679679643389 -0.01244337067512906 … 0.8496862981097764 0.22344233313213632; … ; 5.8922480123539175e-5 0.00014202264594104645 … -0.0016104165385973596 -0.003387682516009194; 6.56362679162351e-5 0.00012655711163273473 … -0.0010322451218726303 -0.0035321440380412135], [-0.3830048919511592 0.97964103525281; -0.27775415596125824 1.6021594916142818; … ; -0.0004341174806268835 -0.0009438170577138625; -0.0003617095226485118 -0.00033849834576412893])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, t, x, uout = lsim(Gd,u,t,x0=x0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the simulation outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots, LaTeXStrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plots.PyPlotBackend()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(t,x, label=[L\"\\beta [rad]\" L\"\\phi [rad]\" L\"p [rad/s]\" L\"r [rad/s]\" L\"\\delta_a\" L\"\\delta_r\" L\"r_w [^\\circ/s]\"], xlabel=\"Time [s]\", ylabel=\"States\",markershape=:circle,linetype=:steppost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(t,y, label=[L\"r_w [^\\circ/s]\" L\"p [^\\circ/s]\" L\"\\beta [^\\circ]\" L\"\\phi [^\\circ]\"], xlabel=\"Time [s]\", ylabel=\"Outputs\",markershape=:circle,linetype=:steppost)"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.3.1",
   "language": "julia",
   "name": "julia-1.3"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.3.1"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}