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diffdrive_control_mpc_alternate.py
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diffdrive_control_mpc_alternate.py
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"""
Example diffdrive_control_mpc.py
Author: Joshua A. Marshall <[email protected]>
GitHub: https://github.com/botprof/agv-examples
"""
# %%
# SIMULATION SETUP
import numpy as np
import matplotlib.pyplot as plt
import cvxpy as cp
from scipy import signal
from mobotpy.models import DiffDrive
from mobotpy.integration import rk_four
# Set the simulation time [s] and the sample period [s]
SIM_TIME = 30.0
T = 0.1
# Create an array of time values [s]
t = np.arange(0.0, SIM_TIME, T)
N = np.size(t)
# %%
# UNCONSTRAINED MPC CONTROLLER DESIGN
# Lookahead time steps
P = 50
# Decide on state and input cost matrices
smallQ = np.diag([1.0, 1.0, 2.0])
smallR = np.diag([1.0, 1.0])
# Create a new desired trajectory time array with sufficient time for the MPC
t_d = np.arange(0.0, SIM_TIME + P * T, T)
# %%
# VEHICLE SETUP
# Set the track length of the vehicle [m]
ELL = 1.0
# Create a vehicle object of type DiffDrive
vehicle = DiffDrive(ELL)
# %%
# COMPUTE THE REFERENCE TRAJECTORY
# Radius of the circle [m]
R = 10.0
# Angular rate [rad/s] at which to traverse the circle
OMEGA = 0.1
# Pre-compute the desired trajectory
x_d = np.zeros((3, N + P))
u_d = np.zeros((2, N + P))
for k in range(0, int(N / 2)):
x_d[0, k] = R * np.sin(OMEGA * t_d[k])
x_d[1, k] = R * (1 - np.cos(OMEGA * t_d[k]))
x_d[2, k] = OMEGA * t_d[k]
u_d[:, k] = vehicle.uni2diff(np.array([R * OMEGA, OMEGA]))
for k in range(int(N / 2), N + P):
x_d[0, k] = x_d[0, k - 1] + R * OMEGA * T
x_d[1, k] = x_d[1, k - 1]
x_d[2, k] = 0
u_d[:, k] = vehicle.uni2diff(np.array([R * OMEGA, 0]))
# %%
# SIMULATE THE CLOSED-LOOP SYSTEM
# Initial conditions
x_init = np.zeros(3)
x_init[0] = 0.0
x_init[1] = 3.0
x_init[2] = 0.0
# Setup some arrays
x = np.zeros((3, N))
u = np.zeros((2, N))
x[:, 0] = x_init
for k in range(1, N):
# Simulate the differential drive vehicle motion
x[:, k] = rk_four(vehicle.f, x[:, k - 1], u[:, k - 1], T)
# Set vectors for optimization
x_MPC = cp.Variable((3, P))
u_MPC = cp.Variable((2, P))
# Initialize the cost function and constraints
J = 0
constraints = []
# For each lookahead step
for j in range(0, P):
# Compute the approximate linearization
A = np.array(
[
[
0,
0,
-0.5
* (u_d[0, k + j - 1] + u_d[1, k + j - 1])
* np.sin(x_d[2, k + j - 1]),
],
[
0,
0,
0.5
* (u_d[0, k + j - 1] + u_d[1, k + j - 1])
* np.cos(x_d[2, k + j - 1]),
],
[0, 0, 0],
]
)
B = np.array(
[
[0.5 * np.cos(x_d[2, k + j - 1]), 0.5 * np.cos(x_d[2, k + j - 1])],
[0.5 * np.sin(x_d[2, k + j - 1]), 0.5 * np.sin(x_d[2, k + j - 1])],
[-1 / ELL, 1 / ELL],
]
)
# Find a discrete time model of the system using zero-order hold
d_model = signal.cont2discrete(
(A, B, np.eye(3), np.zeros((3, 2))), T, method="zoh"
)
F = d_model[0]
G = d_model[1]
# Increment the cost function
J += cp.quad_form(x_MPC[:, j] - x_d[:, k + j], smallQ) + cp.quad_form(
u_MPC[:, j], smallR
)
# Enter the "subject to" constraints
constraints += [
x_MPC[:, j]
== x_d[:, k + j]
+ F @ (x_MPC[:, j - 1] - x_d[:, k + j - 1])
+ G @ (u_MPC[:, j - 1] - u_d[:, k + j - 1])
]
constraints += [x_MPC[:, 0] == x[:, k]]
# constraints += [u_MPC[:, j] <= 1.5 * np.ones(2)]
# constraints += [u_MPC[:, j] >= -1.5 * np.ones(2)]
# Solve the optimization problem
problem = cp.Problem(cp.Minimize(J), constraints)
problem.solve(verbose=False)
# Set the control input to the first element of the solution
u[:, k] = u_MPC[:, 0].value
# %%
# MAKE PLOTS
# Change some plot settings (optional)
plt.rc("text", usetex=True)
plt.rc("text.latex", preamble=r"\usepackage{cmbright,amsmath,bm}")
plt.rc("savefig", format="pdf")
plt.rc("savefig", bbox="tight")
# Plot the states as a function of time
fig1 = plt.figure(1)
fig1.set_figheight(6.4)
ax1a = plt.subplot(411)
plt.plot(t, x_d[0, 0:N], "C1--")
plt.plot(t, x[0, :], "C0")
plt.grid(color="0.95")
plt.ylabel(r"$x$ [m]")
plt.setp(ax1a, xticklabels=[])
plt.legend(["Desired", "Actual"])
ax1b = plt.subplot(412)
plt.plot(t, x_d[1, 0:N], "C1--")
plt.plot(t, x[1, :], "C0")
plt.grid(color="0.95")
plt.ylabel(r"$y$ [m]")
plt.setp(ax1b, xticklabels=[])
ax1c = plt.subplot(413)
plt.plot(t, x_d[2, 0:N] * 180.0 / np.pi, "C1--")
plt.plot(t, x[2, :] * 180.0 / np.pi, "C0")
plt.grid(color="0.95")
plt.ylabel(r"$\theta$ [deg]")
plt.setp(ax1c, xticklabels=[])
ax1d = plt.subplot(414)
plt.step(t, u[0, :], "C2", where="post", label="$v_L$")
plt.step(t, u[1, :], "C3", where="post", label="$v_R$")
plt.grid(color="0.95")
plt.ylabel(r"$\bm{u}$ [m/s]")
plt.xlabel(r"$t$ [s]")
plt.legend()
# Save the plot
# plt.savefig("../agv-book/figs/ch4/control_approx_linearization_fig1.pdf")
# Plot the position of the vehicle in the plane
fig2 = plt.figure(2)
plt.plot(x_d[0, 0:N], x_d[1, 0:N], "C1--", label="Desired")
plt.plot(x[0, :], x[1, :], "C0", label="Actual")
plt.axis("equal")
X_L, Y_L, X_R, Y_R, X_B, Y_B, X_C, Y_C = vehicle.draw(x[0, 0], x[1, 0], x[2, 0])
plt.fill(X_L, Y_L, "k")
plt.fill(X_R, Y_R, "k")
plt.fill(X_C, Y_C, "k")
plt.fill(X_B, Y_B, "C2", alpha=0.5, label="Start")
X_L, Y_L, X_R, Y_R, X_B, Y_B, X_C, Y_C = vehicle.draw(
x[0, N - 1], x[1, N - 1], x[2, N - 1]
)
plt.fill(X_L, Y_L, "k")
plt.fill(X_R, Y_R, "k")
plt.fill(X_C, Y_C, "k")
plt.fill(X_B, Y_B, "C3", alpha=0.5, label="End")
plt.xlabel(r"$x$ [m]")
plt.ylabel(r"$y$ [m]")
plt.legend()
# Save the plot
# plt.savefig("../agv-book/figs/ch4/control_approx_linearization_fig2.pdf")
# Show all the plots to the screen
plt.show()
# %%
# MAKE AN ANIMATION
# Create the animation
ani = vehicle.animate_trajectory(x, x_d, T)
# Create and save the animation
# ani = vehicle.animate_trajectory(
# x, x_d, T, True, "../agv-book/gifs/ch4/control_approx_linearization.gif"
# )
# Show the movie to the screen
# plt.show()
# Show animation in HTML output if you are using IPython or Jupyter notebooks
from IPython.display import display
plt.rc("animation", html="jshtml")
display(ani)
plt.close()