examples/model_predictive_control.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"cruise_control.jl\");\n",
    "state_dim = 4;\n",
    "M = 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s.A = [1.0 -0.5 0.5 0.0; 0.125 0.875 0.125 0.0; 0.125 0.125 0.875 0.5; 0.0 0.0 0.0 0.0]\n",
      "s.A = [1.0 0.0 0.0 0.0; 0.0 1.0 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 0.0]\n"
     ]
    }
   ],
   "source": [
    "for s in system(1, 1).resetmaps\n",
    "    @show s.A\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Mosek\n",
    "using MosekTools\n",
    "sdp_factory = optimizer_with_attributes(Mosek.Optimizer, MOI.Silent() => true);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpcstep (generic function with 1 method)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuMP\n",
    "const MOI = JuMP.MOI\n",
    "const Sets = SOS.SetProg.Sets\n",
    "\n",
    "function mpcstep(hs, modes, x0, y0, soc, usetol)\n",
    "    m = Model(sdp_factory)\n",
    "    nt = length(modes)\n",
    "    yvar = true\n",
    "    if yvar\n",
    "        y = @variable m [1:nt]\n",
    "    else\n",
    "        y = Vector{JuMP.AffExpr}(undef, nt)\n",
    "    end\n",
    "    proju = true\n",
    "    dim = proju ? state_dim - 1 : state_dim\n",
    "    xvar = true\n",
    "    if xvar\n",
    "        x = @variable m [1:nt, 1:dim]\n",
    "    else\n",
    "        x = Matrix{JuMP.AffExpr}(undef, nt, dim)\n",
    "    end\n",
    "    if proju\n",
    "        @variable(m, u[1:(nt-1)], lower_bound=-U, upper_bound=U)\n",
    "    else\n",
    "        u = @variable m [1:(nt-1)]\n",
    "    end\n",
    "    #@show x0\n",
    "    if x0 isa Vector\n",
    "        if xvar\n",
    "            cx0 = @constraint m x[1, :] .== x0[1:dim]\n",
    "        else\n",
    "            x[1, :] .= x0[1:dim]\n",
    "        end\n",
    "    else\n",
    "        if xvar\n",
    "            cx0 = @constraint m x[1, 2:3] .<= x0\n",
    "        else\n",
    "            @variable(m, x_0[1:dim])\n",
    "            cx0 = [@constraint(m, x_0[2] in MOI.LessThan(x0)),\n",
    "                   @constraint(m, x_0[3] in MOI.LessThan(x0))]\n",
    "            x[1, :] = x_0\n",
    "        end\n",
    "    end\n",
    "    if yvar\n",
    "        cy0 = @constraint m y[1] == y0\n",
    "    else\n",
    "        y[1] = y0\n",
    "    end\n",
    "    if usetol\n",
    "        tol = @variable m lower_bound=0\n",
    "    end\n",
    "    cset = Vector{Any}(undef, nt)\n",
    "    cdynx = Vector{Any}(undef, nt-1)\n",
    "    cdyny = Vector{Any}(undef, nt-1)\n",
    "    for i in 1:nt\n",
    "        q = modes[i]\n",
    "        @assert !Polyhedra.hashyperplanes(stateset(hs, q))\n",
    "        if soc === nothing # || true\n",
    "            for h in halfspaces(stateset(hs, q))\n",
    "                # h.a[end] != 0 means that it is u <= 4 or u >= -4 for which we don't want to use tolerance\n",
    "                # It is a hard constraint\n",
    "                # Same for h.a[1] != 0\n",
    "                # @constraint m dot(x[i, :], h.a) <= h.β + (usetol && iszero(h.a[1]) && iszero(h.a[end])? tol : 0.)\n",
    "                @constraint m dot(x[i, :], h.a[1:dim]) <= h.β + (usetol ? tol : 0.)\n",
    "            end\n",
    "        end\n",
    "        if soc !== nothing\n",
    "            set = soc[q]\n",
    "            if proju\n",
    "                # zero_eliminate on Householder gives Householder{Unknown} so let's not project\n",
    "                # but rather introduce a lifting variable\n",
    "                lift_u = @variable(m)\n",
    "                cset[i] = @constraint(m, [x[i, 1:dim]; lift_u] in set)\n",
    "            else\n",
    "                cset[i] = @constraint(m, x[i, 1:dim] in set)\n",
    "            end\n",
    "        end\n",
    "        if i < nt\n",
    "            s = modes[i+1]\n",
    "            edge = Graphs.Edge(q, s)\n",
    "            @assert 1 == length(hs.automaton.Σ[edge])\n",
    "            t = HybridSystems.GraphTransition(edge, first(keys(hs.automaton.Σ[edge])))\n",
    "            σ = symbol(hs.automaton, t)\n",
    "            r = hs.resetmaps[σ]\n",
    "            A = r.A[1:dim, 1:dim]\n",
    "            if proju\n",
    "                B = r.A[1:dim, end]\n",
    "            else\n",
    "                B = r.B\n",
    "            end\n",
    "            if xvar\n",
    "                cdynx[i] = @constraint m x[i+1, :] .== A * x[i, :] + B .* u[i]\n",
    "            else\n",
    "                x[i+1, :] = A * x[i, :] + B .* u[i]\n",
    "            end\n",
    "            if σ == 1\n",
    "                if yvar\n",
    "                    cdyny[i] = @constraint m y[i+1] == y[i] + h * x[i, state_dim-1]\n",
    "                else\n",
    "                    y[i+1] = y[i] + h * x[i, state_dim-1]\n",
    "                end\n",
    "            else\n",
    "                if yvar\n",
    "                    cdyny[i] = @constraint m y[i+1] == y[i]\n",
    "                else\n",
    "                    y[i+1] = y[i]\n",
    "                end\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    @objective(m, Max, y[end] - (usetol ? 1000tol : 0.))\n",
    "    #println(m)\n",
    "    JuMP.optimize!(m)\n",
    "    #@show JuMP.termination_status(m)\n",
    "    #@show JuMP.primal_status(m)\n",
    "    #@show JuMP.dual_status(m)\n",
    "\n",
    "    feasible = JuMP.termination_status(m) != MOI.INFEASIBLE\n",
    "    if feasible\n",
    "        @assert JuMP.termination_status(m) == MOI.OPTIMAL || JuMP.termination_status(m) == MOI.SLOW_PROGRESS\n",
    "        if JuMP.termination_status(m) == MOI.SLOW_PROGRESS\n",
    "            @warn(\"Slow Progress\")\n",
    "        end\n",
    "        if JuMP.primal_status(m) != MOI.FEASIBLE_POINT\n",
    "            error(\"Primal Status: $(JuMP.primal_status(m))\")\n",
    "        end\n",
    "    else\n",
    "        @assert JuMP.dual_status(m) == MOI.INFEASIBILITY_CERTIFICATE\n",
    "        #@show JuMP.dual.(cx0)\n",
    "        #@show JuMP.dual(cy0)\n",
    "        #@show JuMP.dual.(cset)\n",
    "        #@show map(c -> JuMP.dual.(c), cdynx)\n",
    "        #@show JuMP.dual.(cdyny)\n",
    "    end\n",
    "    xval = JuMP.value.(x)[2:end,:]\n",
    "    if proju\n",
    "        xval = [xval JuMP.value.(u)]\n",
    "    end\n",
    "    feasible, xval, JuMP.value.(y)[2:end]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "solvempc (generic function with 2 methods)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function solvempc(nt, nsteps, setvar::Union{Nothing, Function}=nothing; sym=false, kws...)\n",
    "    T = nsteps\n",
    "    na = div(nt,2)-1\n",
    "    nN = nt-T-na\n",
    "    N = 1 + (T+1) * length(v)\n",
    "    startstate = N\n",
    "    modes = [fill(startstate, nN); collect((T+1):-1:2); fill(1, na)]\n",
    "    @assert length(modes) == nt\n",
    "    y = Vector{Float64}(undef, nt)\n",
    "    x = Matrix{Float64}(undef, nt, state_dim)\n",
    "    x[1, :] = [repeat([0., 10.], M); 10.; 0.]\n",
    "    y[1] = -10. * h\n",
    "    last = nt\n",
    "    if setvar !== nothing\n",
    "        #soc = elltosoc.(fullsolve(T, M, h, setvar, volume_heuristic=volume_heuristic,\n",
    "        #                oneshot=false, onlyone=false))\n",
    "        if sym\n",
    "            soc = symsolve(T, M, setvar)\n",
    "        else\n",
    "            soc = fullsolve(T, M, setvar; oneshot=false, onlyone=false, kws...)\n",
    "        end\n",
    "        #soc = fill(soc[1], length(soc))\n",
    "        #return soc\n",
    "    else\n",
    "        soc = nothing\n",
    "    end\n",
    "    hs = system(M, T, N=N)\n",
    "    for i in 1:(nt-1)\n",
    "        #@show i\n",
    "        j = min(nt, i + nsteps)\n",
    "        _step(usetol) = mpcstep(hs, modes[i:j], i == 1 ? zeros(4) : x[i, :], y[i], soc, usetol)\n",
    "        feasible, X, Y = _step(false)\n",
    "        #@show feasible\n",
    "        if !feasible\n",
    "            feasible, X, Y = _step(true)\n",
    "            @show feasible\n",
    "        end\n",
    "        while !feasible && nsteps > 1\n",
    "            nsteps -= 1\n",
    "            ds, X, Y = _step(true)\n",
    "            @show nsteps\n",
    "        end\n",
    "        x[i+1, :] = X[1, :]\n",
    "        y[i+1] = Y[1]\n",
    "        if last == nt && (!feasible || !(X[1, :] in stateset(hs, modes[i+1])))\n",
    "            @show X[1, :]\n",
    "            @show stateset(hs, modes[i+1])\n",
    "            last = i+1\n",
    "            #break\n",
    "        end\n",
    "    end\n",
    "    if last < nt\n",
    "        @warn(\"$last < $nt\")\n",
    "    end\n",
    "    take = last\n",
    "    @show nt\n",
    "    @show take\n",
    "    @show last\n",
    "    #take = nt\n",
    "    if take > nN\n",
    "        [x[2:nN, :]; x[(nN+2):take, :]], [y[2:nN]; y[(nN+2):take]]\n",
    "    else\n",
    "        x[2:take, :], y[2:take]\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "if true\n",
    "    minsafe = 5\n",
    "    nt = 40\n",
    "else\n",
    "    #nt = 77\n",
    "    minsafe = 24\n",
    "    nt = 152\n",
    "end\n",
    "tswitch = fill(h * (nt-2) / 2, 2)\n",
    "t = [0.]\n",
    "for i in 2:(nt-2)\n",
    "    push!(t, t[end] + h)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using DataStructures\n",
    "XY = OrderedDict{String, Tuple{Matrix{Float64}, Vector{Float64}}}();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×4 Array{Float64,2}:\n",
       " 1.0  -0.5  0.5  0.0\n",
       " 0.5   0.5  0.5  0.0\n",
       " 0.5   0.5  0.5  0.5\n",
       " 0.0   0.0  0.0  0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system(M, minsafe-1).resetmaps[1].A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "X[1, :] = [-0.991988, 1.26074, 1.26074, 0.887088]\n",
      "stateset(hs, modes[i + 1]) = HalfSpace([-1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, -1.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, 1.0], 1.0) ∩ HalfSpace([0.0, -1.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, -1.0, 0.0], 1.0) ∩ HalfSpace([0.0, 1.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, 1.0, 0.0], 1.0)\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "feasible = true\n",
      "nt = 40\n",
      "take = 22\n",
      "last = 22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: 22 < 40\n",
      "└ @ Main In[4]:53\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([0.0 0.0 0.5 1.0; 0.25 0.125 0.875 1.0; … ; -0.548444 1.61963 0.73254 -1.0; -0.991988 1.26074 1.26074 0.887088], [-5.0, -4.75, -4.3125, -3.75, -3.39063, -2.90625, -2.29688, -1.8125, -1.30035, -0.627894, 0.159916, 0.983089, 1.83573, 2.71294, 3.71294, 4.60549, 5.53028, 6.2373, 6.77697, 7.14324])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY[\"unsafe\"] = solvempc(nt, minsafe-1)\n",
    "#@assert 55 == XYok[\"unsafe\"][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nstates(hs) = 6\n",
      "Computing set 1\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0036869049072265625\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.8339403023509967\n",
      "Computing set 2\n",
      "MOI.get(model, MOI.SolveTime()) = 0.003571033477783203\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.8788411310003909\n",
      "Computing set 3\n",
      "MOI.get(model, MOI.SolveTime()) = 0.003446817398071289\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.8371623954122568\n",
      "Computing set 4\n",
      "MOI.get(model, MOI.SolveTime()) = 0.003213167190551758\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.8001194452813638\n",
      "Computing set 5\n",
      "MOI.get(model, MOI.SolveTime()) = 0.00494694709777832\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.7702865569089623\n",
      "Computing set 6\n",
      "MOI.get(model, MOI.SolveTime()) = 0.030493974685668945\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 0.7224048156321096\n",
      "nt = 40\n",
      "take = 40\n",
      "last = 40\n"
     ]
    }
   ],
   "source": [
    "XY[\"ellipsoids\"] = solvempc(nt, minsafe-1, h -> Ellipsoid(point=h), volume_heuristic=nth_root);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nstates(hs) = 6\n",
      "Computing set 1\n",
      "MOI.get(model, MOI.SolveTime()) = 0.002056121826171875\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 2.6666666667149035\n",
      "Computing set 2\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0030601024627685547\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 2.8685537548390556\n",
      "Computing set 3\n",
      "MOI.get(model, MOI.SolveTime()) = 0.003225088119506836\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 2.8734741948057105\n",
      "Computing set 4\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0028231143951416016\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 2.5012108881609194\n",
      "Computing set 5\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0031588077545166016\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 2.4116449286888977\n",
      "Computing set 6\n",
      "MOI.get(model, MOI.SolveTime()) = 0.004078865051269531\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 1.7060406197803495\n",
      "nt = 40\n",
      "take = 40\n",
      "last = 40\n"
     ]
    }
   ],
   "source": [
    "XY[\"quadratic\"] = solvempc(nt, minsafe-1, h -> Ellipsoid(point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nstates(hs) = 6\n",
      "Computing set 1\n",
      "MOI.get(model, MOI.SolveTime()) = 0.002460956573486328\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 21.33333333944897\n",
      "Computing set 2\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0034339427947998047\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 22.912893672769144\n",
      "Computing set 3\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0030961036682128906\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 22.93651866582841\n",
      "Computing set 4\n",
      "MOI.get(model, MOI.SolveTime()) = 0.003412008285522461\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 19.95848297705915\n",
      "Computing set 5\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0033621788024902344\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 19.22527790973345\n",
      "Computing set 6\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0050089359283447266\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 13.593530319267968\n",
      "X[1, :] = [0.114314, 0.110873, 0.815403, 1.0]\n",
      "stateset(hs, modes[i + 1]) = HalfSpace([-1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, -1.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, 1.0], 1.0) ∩ HalfSpace([0.0, -1.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, -1.0, 0.0], 1.0) ∩ HalfSpace([0.0, 1.0, 0.0, 0.0], 2.0) ∩ HalfSpace([0.0, 0.0, 1.0, 0.0], 2.0)\n",
      "nt = 40\n",
      "take = 21\n",
      "last = 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: 21 < 40\n",
      "└ @ Main In[4]:53\n"
     ]
    }
   ],
   "source": [
    "XY[\"quadratic\"]  = solvempc(nt, minsafe-1, h -> PolySet(convex=true, degree=2, point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nstates(hs) = 6\n",
      "Computing set 1\n",
      "MOI.get(model, MOI.SolveTime()) = 0.0632789134979248\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 76.79558436810976\n",
      "Computing set 2\n",
      "MOI.get(model, MOI.SolveTime()) = 0.062744140625\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 82.80576474211729\n",
      "Computing set 3\n",
      "MOI.get(model, MOI.SolveTime()) = 0.05501103401184082\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 84.74455995222735\n",
      "Computing set 4\n",
      "MOI.get(model, MOI.SolveTime()) = 0.06474900245666504\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 87.38287715021103\n",
      "Computing set 5\n",
      "MOI.get(model, MOI.SolveTime()) = 0.05719614028930664\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 90.90612623333243\n",
      "Computing set 6\n",
      "MOI.get(model, MOI.SolveTime()) = 0.10369396209716797\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 89.87580888505528\n",
      "nt = 40\n",
      "take = 40\n",
      "last = 40\n"
     ]
    }
   ],
   "source": [
    "XY[\"quartic\"] = solvempc(nt, minsafe-1, h -> PolySet(convex=true, degree=4, point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nstates(hs) = 6\n",
      "Computing set 1\n",
      "MOI.get(model, MOI.SolveTime()) = 4.712260007858276\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 684.878785134573\n",
      "Computing set 2\n",
      "MOI.get(model, MOI.SolveTime()) = 3.0121190547943115\n",
      "JuMP.termination_status(model) = OPTIMAL::TerminationStatusCode = 1\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 770.411846875277\n",
      "Computing set 3\n",
      "MOI.get(model, MOI.SolveTime()) = 3.799837827682495\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 789.036562180584\n",
      "Computing set 4\n",
      "MOI.get(model, MOI.SolveTime()) = 2.9110279083251953\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 820.4258185028393\n",
      "Computing set 5\n",
      "MOI.get(model, MOI.SolveTime()) = 3.6349661350250244\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 857.0698257834799\n",
      "Computing set 6\n",
      "MOI.get(model, MOI.SolveTime()) = 3.5855040550231934\n",
      "JuMP.termination_status(model) = SLOW_PROGRESS::TerminationStatusCode = 19\n",
      "JuMP.primal_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.dual_status(model) = FEASIBLE_POINT::ResultStatusCode = 1\n",
      "JuMP.objective_value(model) = 803.5805727402095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: Slow Progress\n",
      "└ @ Main In[40]:122\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nt = 40\n",
      "take = 40\n",
      "last = 40\n"
     ]
    }
   ],
   "source": [
    "XY[\"sextic\"] = solvempc(nt, minsafe-1, h -> PolySet(convex=true, degree=6, point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#XYok[\"octic\"] = solvempc(nt, minsafe-1, h -> PolySet(convex=true, degree=8, point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "limit_line (generic function with 2 methods)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Plots\n",
    "Plots.pyplot()\n",
    "_savefig(name) = Plots.savefig(\"/home/blegat/Dropbox/Research/Images/CruiseControlPoly$name.eps\");\n",
    "#XY = [(\"unsafe 8\", XYko[\"unsafe 8\"])];\n",
    "XY = XYok;\n",
    "plotsize = (800, 400);\n",
    "# colors taken from https://previews.123rf.com/images/capacitorphoto/capacitorphoto1410/capacitorphoto141000191/32438941-Graph-Icon-color-set-illustration--Stock-Vector.jpg\n",
    "red = Plots.RGBA(((0xf5, 0x92, 0x97) ./ 255)...)\n",
    "gre = Plots.RGBA(((0xcb, 0xdf, 0x80) ./ 255)...)\n",
    "blu = Plots.RGBA(((0x23, 0x94, 0xce) ./ 255)...)\n",
    "ora = Plots.RGBA(((0xfa, 0xcb, 0x95) ./ 255)...)\n",
    "yel = Plots.RGBA(((0xf2, 0xf0, 0x8b) ./ 255)...)\n",
    "colors = Dict(\"unsafe\" => blu, \"ellipsoids\" => gre, \"quadratic\" => yel,\n",
    "              \"quartic\" => ora, \"sextic\" => red);\n",
    "function baseplot(ylabel, yticks)\n",
    "    Plots.plot(legendfont=Plots.font(13), guidefont=Plots.font(16), tickfont=Plots.font(10), grid=false,\n",
    "               ylabel=ylabel, yticks=yticks, size=plotsize, xlabel=\"Time [s]\")\n",
    "end\n",
    "function limit_line(t1, t2, y)\n",
    "    Plots.plot!([t1, t2], [y, y], linewidth=0.5, color=:black, label=\"\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 800x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseplot(\"Speed [m/s]\", v_shift .+ [vmin, (vmin + v[1])/2, v[1], vmax])\n",
    "limit_line(t[1], tswitch[1], v_shift + vmax)\n",
    "limit_line(tswitch[1], t[end], v_shift + v[1])\n",
    "mini = Inf\n",
    "maxi = 35\n",
    "for (s, (x, y)) in XY\n",
    "    v1 = x[:, state_dim-2]\n",
    "    v0 = x[:, state_dim-1]\n",
    "    #:none,:auto,:circle,:rect,:star5,:diamond,:hexagon,:cross,:xcross,:utriangle,:dtriangle,:rtriangle,:ltriangle,:pentagon,:heptagon,:octagon,:star4,:star6,:star7,:star8,:vline,:hline,:+,:x].\n",
    "    issafe = s[1] != 'u'\n",
    "    ms = 7 + (issafe ? 0 : 1)\n",
    "    #Plots.scatter!(t[1:end-1], v1[1:end], label=\"Trailer speed $s\", markersize=ms, markershape=issafe ? :diamond : :utriangle, markerstrokewidth=0)\n",
    "    v0 = v0[1:end-1]\n",
    "    Plots.scatter!(t[1:(length(v0))], v_shift .+ v0, color = colors[s], label=\"Truck speed $s\",\n",
    "                   markersize=ms, markershape=issafe ? :circle : :rtriangle, markerstrokewidth=0)\n",
    "    mini = min(mini, minimum(v1), minimum(v0))\n",
    "    maxi = max(maxi, maximum(v1), maximum(v0))\n",
    "end\n",
    "#Plots.plot!(tswitch, [mini, maxi])\n",
    "#Plots.plot!(tswitch, [mini, maxi], linewidth=2, label=\"\")\n",
    "if length(XY) == 1\n",
    "    #_savefig(\"UnsafeSpeed\")\n",
    "else\n",
    "    #@assert length(XY) == 3\n",
    "    #_savefig(\"Speed\")\n",
    "end\n",
    "_savefig(\"Speed\")\n",
    "Plots.plot!()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 800x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "baseplot(\"Displacement [m]\", [-D, -D/2, 0.0, D/2, D]) #, ylims=(-(D+0.05), D+0.55))\n",
    "limit_line(t[1], t[end], -D)\n",
    "limit_line(t[1], t[end], D)\n",
    "for (s, (x, y)) in XY\n",
    "    dist = x[:, 1]\n",
    "    issafe = s[1] != 'u'\n",
    "    ms = 7 + (issafe ? 0 : 1)\n",
    "    if !issafe\n",
    "        # Choose from :none,:auto,:circle,:rect,:star5,:diamond,:hexagon,:cross,:xcross,:utriangle,:dtriangle,:rtriangle,:ltriangle,:pentagon,:heptagon,:octagon,:star4,:star6,:star7,:star8,:vline,:hline,:+,:x].\n",
    "\n",
    "        Plots.scatter!([t[length(dist)]], [dist[end]], markersize=ms+5, markerstrokewidth=3,\n",
    "            markershape=:circle, markercolor=:white, markerstrokecolor = red, label=\"\")#, label=\"Constraint violation for $s\")\n",
    "    end\n",
    "    Plots.scatter!(t[1:length(dist)], dist, label=\"Spring elongation $s\", color = colors[s],\n",
    "        markersize=ms, markershape=issafe ? :circle : :rtriangle, markerstrokewidth=0)\n",
    "end\n",
    "Plots.plot!()\n",
    "#Plots.plot!(tswitch, [-.5, .5], label=\"\")\n",
    "_savefig(\"Elongation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plots.plot(legendfont=Plots.font(13), guidefont=Plots.font(16), tickfont=Plots.font(10), size=plotsize, grid=false, ylim=(-4.3, 4.3), xlabel=\"Time [s]\", ylabel=\"Acceleration [m/s²]\")\n",
    "Plots.plot!([0, t[end]], [-4, -4], linewidth=0.5, color=:black, label=\"\")\n",
    "Plots.plot!([0, t[end]], [4, 4], linewidth=0.5, color=:black, label=\"\")\n",
    "for (s, (x, y)) in XY\n",
    "    u = x[:, 4]\n",
    "    issafe = s[1] != 'u'\n",
    "    u = u[1:end-2]\n",
    "    ms = 7 + (issafe ? 0 : 1)\n",
    "    if !issafe\n",
    "        # Choose from :none,:auto,:circle,:rect,:star5,:diamond,:hexagon,:cross,:xcross,:utriangle,:dtriangle,:rtriangle,:ltriangle,:pentagon,:heptagon,:octagon,:star4,:star6,:star7,:star8,:vline,:hline,:+,:x].\n",
    "\n",
    "        #Plots.scatter!([t[length(u)]], [u[end]], markersize=ms+5, markerstrokewidth=3, markershape=:circle, markercolor=:white, markerstrokecolor = red, label=\"Constraint violation for $s\")\n",
    "    end\n",
    "    Plots.scatter!(t[1:length(u)], u, label=\"Truck acceleration $s\", color = colors[s],\n",
    "        markersize=ms, markershape=issafe ? :circle : :rtriangle, markerstrokewidth=0)\n",
    "    Plots.scatter!()\n",
    "end\n",
    "#Plots.plot!(tswitch, [-U, U], label=\"\")\n",
    "if length(XY) == 1\n",
    "    #_savefig(\"UnsafeAcceleration\")\n",
    "else\n",
    "    #@assert length(XY) == 3\n",
    "    #_savefig(\"Acceleration\")\n",
    "end\n",
    "Plots.plot!()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plots.plot(size=(800, 400), xlabel=\"Time [s]\", ylabel=\"Truck position [m]\")\n",
    "for (s, (x, y)) in XY\n",
    "    Plots.plot!(t, y, label=s)\n",
    "end\n",
    "Plots.plot!()\n",
    "#Plots.plot!(tswitch, collect(extrema(XY[\"unsafe optimal\"][2])), label=\"\")\n",
    "#_savefig(\"Position\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sols = fullsolve(minsafe-1, M, h -> Ellipsoid(point=h), volume_heuristic=nth_root, oneshot=false, onlyone=false);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sols_4 = solvempc(nt, minsafe-1, h -> PolySet(convex=true, degree=4, point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sols_2 = solvempc(nt, minsafe-1, h -> Ellipsoid(point=h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length(sols) = 73\n",
      "((SOS.SetProg).Sets).dimension(sols[1]) = 4\n",
      "stateset(system(M, minsafe - 1), 1) = HalfSpace([-1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([1.0, 0.0, 0.0, 0.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, -1.0], 1.0) ∩ HalfSpace([0.0, 0.0, 0.0, 1.0], 1.0) ∩ HalfSpace([0.0, -1.0, 0.0, 0.0], -1.0) ∩ HalfSpace([0.0, 0.0, -1.0, 0.0], -1.0) ∩ HalfSpace([0.0, 1.0, 0.0, 0.0], 15.6) ∩ HalfSpace([0.0, 0.0, 1.0, 0.0], 15.6)\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "@show length(sols)\n",
    "@show SOS.SetProg.Sets.dimension(sols[1])\n",
    "@show stateset(system(M, minsafe-1), 1)\n",
    "plot(project(sols[1], [1, 2]), npoints=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length(sols_4) = 4\n"
     ]
    },
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       "  1693.5,1450.62 1678.39,1448.88 1661.33,1446.81 1641.92,1444.31 1619.67,1441.26 1593.95,1437.49 1563.96,1432.79 1528.65,1426.85 1486.71,1419.25 1436.45,1409.37 \n",
       "  1375.76,1396.39 1302.17,1379.13 1213.14,1356.07 1106.87,1325.35 984.114,1285.3 850.961,1235.5 720.555,1178.45 608.457,1119.46 517.706,1059.67 424.433,977.607 \n",
       "  303.267,817.545 209.52,569.712 209.52,353.878 \n",
       "  \"/>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "@show length(sols_4)\n",
    "plot(project(sols_4[1], [1, 3]), npoints=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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