mpc_python_learn/notebooks/2.1-MPC-with-iterative-line...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterative Linearization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal is to have a more accurate linearization of the diff equations. For every time step the optimization is iterativelly repeated using he previous optimization results **u_bar** to approximate the vehicle dynamics, instead of a random starting guess and/or the rsult at time t-1.\n",
"\n",
"In previous case the results at t-1 wer used to approimate the dynamics art time t!\n",
"\n",
"This maks the results less correlated but makes the controller slower!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.integrate import odeint\n",
"from scipy.interpolate import interp1d\n",
"import cvxpy as cp\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Control problem statement.\n",
"\"\"\"\n",
"\n",
"N = 4 # number of state variables\n",
"M = 2 # number of control variables\n",
"T = 20 # Prediction Horizon\n",
"DT = 0.2 # discretization step\n",
"\n",
"\n",
"def get_linear_model(x_bar, u_bar):\n",
" \"\"\"\n",
" Computes the LTI approximated state space model x' = Ax + Bu + C\n",
" \"\"\"\n",
"\n",
" L = 0.3 # vehicle wheelbase\n",
"\n",
" x = x_bar[0]\n",
" y = x_bar[1]\n",
" v = x_bar[2]\n",
" theta = x_bar[3]\n",
"\n",
" a = u_bar[0]\n",
" delta = u_bar[1]\n",
"\n",
" A = np.zeros((N, N))\n",
" A[0, 2] = np.cos(theta)\n",
" A[0, 3] = -v * np.sin(theta)\n",
" A[1, 2] = np.sin(theta)\n",
" A[1, 3] = v * np.cos(theta)\n",
" A[3, 2] = v * np.tan(delta) / L\n",
" A_lin = np.eye(N) + DT * A\n",
"\n",
" B = np.zeros((N, M))\n",
" B[2, 0] = 1\n",
" B[3, 1] = v / (L * np.cos(delta) ** 2)\n",
" B_lin = DT * B\n",
"\n",
" f_xu = np.array(\n",
" [v * np.cos(theta), v * np.sin(theta), a, v * np.tan(delta) / L]\n",
" ).reshape(N, 1)\n",
" C_lin = DT * (\n",
" f_xu - np.dot(A, x_bar.reshape(N, 1)) - np.dot(B, u_bar.reshape(M, 1))\n",
" )\n",
"\n",
" return np.round(A_lin, 4), np.round(B_lin, 4), np.round(C_lin, 4)\n",
"\n",
"\n",
"\"\"\"\n",
"the ODE is used to update the simulation given the mpc results\n",
"I use this insted of using the LTI twice\n",
"\"\"\"\n",
"\n",
"\n",
"def kinematics_model(x, t, u):\n",
" \"\"\"\n",
" Returns the set of ODE of the vehicle model.\n",
" \"\"\"\n",
"\n",
" L = 0.3 # vehicle wheelbase\n",
" dxdt = x[2] * np.cos(x[3])\n",
" dydt = x[2] * np.sin(x[3])\n",
" dvdt = u[0]\n",
" dthetadt = x[2] * np.tan(u[1]) / L\n",
"\n",
" dqdt = [dxdt, dydt, dvdt, dthetadt]\n",
"\n",
" return dqdt\n",
"\n",
"\n",
"def predict(x0, u):\n",
" \"\"\" \"\"\"\n",
"\n",
" x_ = np.zeros((N, T + 1))\n",
"\n",
" x_[:, 0] = x0\n",
"\n",
" # solve ODE\n",
" for t in range(1, T + 1):\n",
"\n",
" tspan = [0, DT]\n",
" x_next = odeint(kinematics_model, x0, tspan, args=(u[:, t - 1],))\n",
"\n",
" x0 = x_next[1]\n",
" x_[:, t] = x_next[1]\n",
"\n",
" return x_\n",
"\n",
"\n",
"def compute_path_from_wp(start_xp, start_yp, step=0.1):\n",
" \"\"\"\n",
" Computes a reference path given a set of waypoints\n",
" \"\"\"\n",
"\n",
" final_xp = []\n",
" final_yp = []\n",
" delta = step # [m]\n",
"\n",
" for idx in range(len(start_xp) - 1):\n",
" section_len = np.sum(\n",
" np.sqrt(\n",
" np.power(np.diff(start_xp[idx : idx + 2]), 2)\n",
" + np.power(np.diff(start_yp[idx : idx + 2]), 2)\n",
" )\n",
" )\n",
"\n",
" interp_range = np.linspace(0, 1, np.floor(section_len / delta).astype(int))\n",
"\n",
" fx = interp1d(np.linspace(0, 1, 2), start_xp[idx : idx + 2], kind=1)\n",
" fy = interp1d(np.linspace(0, 1, 2), start_yp[idx : idx + 2], kind=1)\n",
"\n",
" final_xp = np.append(final_xp, fx(interp_range))\n",
" final_yp = np.append(final_yp, fy(interp_range))\n",
"\n",
" dx = np.append(0, np.diff(final_xp))\n",
" dy = np.append(0, np.diff(final_yp))\n",
" theta = np.arctan2(dy, dx)\n",
"\n",
" return np.vstack((final_xp, final_yp, theta))\n",
"\n",
"\n",
"def get_nn_idx(state, path):\n",
" \"\"\"\n",
" Computes the index of the waypoint closest to vehicle\n",
" \"\"\"\n",
"\n",
" dx = state[0] - path[0, :]\n",
" dy = state[1] - path[1, :]\n",
" dist = np.hypot(dx, dy)\n",
" nn_idx = np.argmin(dist)\n",
"\n",
" try:\n",
" v = [\n",
" path[0, nn_idx + 1] - path[0, nn_idx],\n",
" path[1, nn_idx + 1] - path[1, nn_idx],\n",
" ]\n",
" v /= np.linalg.norm(v)\n",
"\n",
" d = [path[0, nn_idx] - state[0], path[1, nn_idx] - state[1]]\n",
"\n",
" if np.dot(d, v) > 0:\n",
" target_idx = nn_idx\n",
" else:\n",
" target_idx = nn_idx + 1\n",
"\n",
" except IndexError as e:\n",
" target_idx = nn_idx\n",
"\n",
" return target_idx\n",
"\n",
"\n",
"def get_ref_trajectory(state, path, target_v):\n",
" \"\"\"\n",
" Adapted from pythonrobotics\n",
" \"\"\"\n",
" xref = np.zeros((N, T + 1))\n",
" dref = np.zeros((1, T + 1))\n",
"\n",
" # sp = np.ones((1,T +1))*target_v #speed profile\n",
"\n",
" ncourse = path.shape[1]\n",
"\n",
" ind = get_nn_idx(state, path)\n",
"\n",
" xref[0, 0] = path[0, ind] # X\n",
" xref[1, 0] = path[1, ind] # Y\n",
" xref[2, 0] = target_v # sp[ind] #V\n",
" xref[3, 0] = path[2, ind] # Theta\n",
" dref[0, 0] = 0.0 # steer operational point should be 0\n",
"\n",
" dl = 0.05 # Waypoints spacing [m]\n",
" travel = 0.0\n",
"\n",
" for i in range(T + 1):\n",
" travel += abs(target_v) * DT # current V or target V?\n",
" dind = int(round(travel / dl))\n",
"\n",
" if (ind + dind) < ncourse:\n",
" xref[0, i] = path[0, ind + dind]\n",
" xref[1, i] = path[1, ind + dind]\n",
" xref[2, i] = target_v # sp[ind + dind]\n",
" xref[3, i] = path[2, ind + dind]\n",
" dref[0, i] = 0.0\n",
" else:\n",
" xref[0, i] = path[0, ncourse - 1]\n",
" xref[1, i] = path[1, ncourse - 1]\n",
" xref[2, i] = 0.0 # stop? #sp[ncourse - 1]\n",
" xref[3, i] = path[2, ncourse - 1]\n",
" dref[0, i] = 0.0\n",
"\n",
" return xref, dref"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-2-e22409964dd8>:127: RuntimeWarning: invalid value encountered in true_divide\n",
" v /= np.linalg.norm(v)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CVXPY Optimization Time: Avrg: 0.5979s Max: 0.8275s Min: 0.2939s\n"
]
}
],
"source": [
"track = compute_path_from_wp(\n",
" [0, 3, 4, 6, 10, 12, 14, 6, 1, 0], [0, 0, 2, 4, 3, 3, -2, -6, -2, -2], 0.05\n",
")\n",
"\n",
"# track = compute_path_from_wp([0,10,10,0],\n",
"# [0,0,1,1],0.05)\n",
"\n",
"sim_duration = 200 # time steps\n",
"opt_time = []\n",
"\n",
"x_sim = np.zeros((N, sim_duration))\n",
"u_sim = np.zeros((M, sim_duration - 1))\n",
"\n",
"MAX_SPEED = 1.5 # m/s\n",
"MAX_ACC = 1.0 # m/ss\n",
"MAX_D_ACC = 1.0 # m/sss\n",
"MAX_STEER = np.radians(30) # rad\n",
"MAX_D_STEER = np.radians(30) # rad/s\n",
"\n",
"REF_VEL = 1.0 # m/s\n",
"\n",
"# Starting Condition\n",
"x0 = np.zeros(N)\n",
"x0[0] = 0 # x\n",
"x0[1] = -0.25 # y\n",
"x0[2] = 0.0 # v\n",
"x0[3] = np.radians(-0) # yaw\n",
"\n",
"for sim_time in range(sim_duration - 1):\n",
"\n",
" iter_start = time.time()\n",
"\n",
" # starting guess for ctrl\n",
" u_bar = np.zeros((M, T))\n",
" u_bar[0, :] = MAX_ACC / 2 # a\n",
" u_bar[1, :] = 0.0 # delta\n",
"\n",
" for _ in range(5):\n",
" u_prev = u_bar\n",
"\n",
" # dynamics starting state\n",
" x_bar = np.zeros((N, T + 1))\n",
" x_bar[:, 0] = x_sim[:, sim_time]\n",
"\n",
" # prediction for linearization of costrains\n",
" for t in range(1, T + 1):\n",
" xt = x_bar[:, t - 1].reshape(N, 1)\n",
" ut = u_bar[:, t - 1].reshape(M, 1)\n",
" A, B, C = get_linear_model(xt, ut)\n",
" xt_plus_one = np.squeeze(np.dot(A, xt) + np.dot(B, ut) + C)\n",
" x_bar[:, t] = xt_plus_one\n",
"\n",
" # CVXPY Linear MPC problem statement\n",
" x = cp.Variable((N, T + 1))\n",
" u = cp.Variable((M, T))\n",
" cost = 0\n",
" constr = []\n",
"\n",
" # Cost Matrices\n",
" Q = np.diag([20, 20, 10, 0]) # state error cost\n",
" Qf = np.diag([30, 30, 30, 0]) # state final error cost\n",
" R = np.diag([10, 10]) # input cost\n",
" R_ = np.diag([10, 10]) # input rate of change cost\n",
"\n",
" # Get Reference_traj\n",
" x_ref, d_ref = get_ref_trajectory(x_bar[:, 0], track, REF_VEL)\n",
"\n",
" # Prediction Horizon\n",
" for t in range(T):\n",
"\n",
" # Tracking Error\n",
" cost += cp.quad_form(x[:, t] - x_ref[:, t], Q)\n",
"\n",
" # Actuation effort\n",
" cost += cp.quad_form(u[:, t], R)\n",
"\n",
" # Actuation rate of change\n",
" if t < (T - 1):\n",
" cost += cp.quad_form(u[:, t + 1] - u[:, t], R_)\n",
" constr += [\n",
" cp.abs(u[0, t + 1] - u[0, t]) / DT <= MAX_D_ACC\n",
" ] # max acc rate of change\n",
" constr += [\n",
" cp.abs(u[1, t + 1] - u[1, t]) / DT <= MAX_D_STEER\n",
" ] # max steer rate of change\n",
"\n",
" # Kinrmatics Constrains (Linearized model)\n",
" A, B, C = get_linear_model(x_bar[:, t], u_bar[:, t])\n",
" constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C.flatten()]\n",
"\n",
" # Final Point tracking\n",
" cost += cp.quad_form(x[:, T] - x_ref[:, T], Qf)\n",
"\n",
" # sums problem objectives and concatenates constraints.\n",
" constr += [x[:, 0] == x_bar[:, 0]] # starting condition\n",
" constr += [x[2, :] <= MAX_SPEED] # max speed\n",
" constr += [x[2, :] >= 0.0] # min_speed (not really needed)\n",
" constr += [cp.abs(u[0, :]) <= MAX_ACC] # max acc\n",
" constr += [cp.abs(u[1, :]) <= MAX_STEER] # max steer\n",
"\n",
" # Solve\n",
" prob = cp.Problem(cp.Minimize(cost), constr)\n",
" solution = prob.solve(solver=cp.OSQP, verbose=False)\n",
"\n",
" # retrieved optimized U and assign to u_bar to linearize in next step\n",
" u_bar = np.vstack(\n",
" (np.array(u.value[0, :]).flatten(), (np.array(u.value[1, :]).flatten()))\n",
" )\n",
"\n",
" # check how this solution differs from previous\n",
" # if the solutions are very\n",
" delta_u = np.sum(np.sum(np.abs(u_bar - u_prev), axis=0), axis=0)\n",
" if delta_u < 0.05:\n",
" break\n",
"\n",
" # select u from best iteration\n",
" u_sim[:, sim_time] = u_bar[:, 0]\n",
"\n",
" # Measure elpased time to get results from cvxpy\n",
" opt_time.append(time.time() - iter_start)\n",
"\n",
" # move simulation to t+1\n",
" tspan = [0, DT]\n",
" x_sim[:, sim_time + 1] = odeint(\n",
" kinematics_model, x_sim[:, sim_time], tspan, args=(u_bar[:, 0],)\n",
" )[1]\n",
"\n",
" # reset u_bar? -> this simulates that we don use previous solution!\n",
" u_bar[0, :] = MAX_ACC / 2 # a\n",
" u_bar[1, :] = 0.0 # delta\n",
"\n",
"\n",
"print(\n",
" \"CVXPY Optimization Time: Avrg: {:.4f}s Max: {:.4f}s Min: {:.4f}s\".format(\n",
" np.mean(opt_time), np.max(opt_time), np.min(opt_time)\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1080x720 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot trajectory\n",
"grid = plt.GridSpec(4, 5)\n",
"\n",
"plt.figure(figsize=(15, 10))\n",
"\n",
"plt.subplot(grid[0:4, 0:4])\n",
"plt.plot(track[0, :], track[1, :], \"b+\")\n",
"plt.plot(x_sim[0, :], x_sim[1, :])\n",
"plt.axis(\"equal\")\n",
"plt.ylabel(\"y\")\n",
"plt.xlabel(\"x\")\n",
"\n",
"plt.subplot(grid[0, 4])\n",
"plt.plot(u_sim[0, :])\n",
"plt.ylabel(\"a(t) [m/ss]\")\n",
"\n",
"plt.subplot(grid[1, 4])\n",
"plt.plot(x_sim[2, :])\n",
"plt.ylabel(\"v(t) [m/s]\")\n",
"\n",
"plt.subplot(grid[2, 4])\n",
"plt.plot(np.degrees(u_sim[1, :]))\n",
"plt.ylabel(\"delta(t) [rad]\")\n",
"\n",
"plt.subplot(grid[3, 4])\n",
"plt.plot(x_sim[3, :])\n",
"plt.ylabel(\"theta(t) [rad]\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:.conda-jupyter] *",
"language": "python",
"name": "conda-env-.conda-jupyter-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}