d = pickle.load(f)
sigma = d['sigma']
b = d['b']
r = d['r']
lrz = Lorenz(sigma, b, r)
lrz.X = d['X']
lrz.U = d['U']
""" Initialize lorenz object state and compute trajectories with learning based control, lyapunov based control,
and without any control """
n = 6000 # number of time steps
lrz.state = [-4, -4, -1]
y_l, u_l, t_l = lrz.trajectory(n, 0)
lrz.state = [-4, -4, -1]
y_m, u_m, t_m = lrz.trajectory(n, 1)
lrz.state = [-4, -4, -1]
y_wc, t_wc = lrz.trajectory_no_control(n)
""" trajectory visualization """
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
ax.plot(y_wc[:, 0],
y_wc[:, 1],
y_wc[:, 2],
'r',
linewidth=2,
label="uncontrolled trajectory")
ax.plot(y_l[:, 0],
y_l[:, 1],
y_l[:, 2],
'k',
linewidth=2,
label="learning based control")
