Poisoning-Attacks-Data-Driven-Control/bode_plot_targeted.py at master · rssalessio/Poisoning-Attacks-Data-Driven-Control · GitHub

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import control as ctl
import numpy as np
import matplotlib.pyplot as plt
from control.matlab import step


plt.rcParams.update({
    "font.family":
    "serif",  # use serif/main font for text elements
    "text.usetex":
    True,  # use inline math for ticks
    "pgf.rcfonts":
    False,  # don't setup fonts from rc parameters
    "pgf.preamble": [
        "\\usepackage{units}",  # load additional packages
        "\\usepackage{metalogo}",
        "\\usepackage{unicode-math}",  # unicode math setup
        r"\setmathfont{xits-math.otf}",
        r"\setmainfont{DejaVu Serif}",  # serif font via preamble
    ]
})

def calcControl(theta, num, den):
		return np.dot(theta, num)[0, 0].minreal() * den


Ts = 0.05
omega = 10
alpha = np.exp(-Ts * omega)
refModel = ctl.tf([(1 - alpha)**2], [1, -2 * alpha, alpha**2, 0], Ts)
den = [1, -1.41833, 1.58939, -1.31608, 0.88642]
num = [0.28261, 0.50666]
G=ctl.tf(num, den, Ts)


# xi = 0.05
# omegan = 0.9

# p1 = -2 * np.exp(-xi * Ts * omegan) * np.cos(omegan * Ts * np.sqrt(1- xi**2))
# p2 = np.exp(-2 * xi * omegan * Ts)

# den = [1,  p1, p2]
# delta = ctl.tf([1, -1], den, Ts)
# refModel2 = refModel +  1e-2 * delta

# Control base
den = ctl.tf([1], [1, -1], Ts)
z = ctl.tf([1, 0], [1], Ts)
base = [z, 1, 1 / z, 1 / (z * z), 1 / (z * z * z), 1 / (z * z * z * z)]
baseDen = [i * den for i in base]

theta_orig = np.array([0.33324, -0.60964, 0.72401, -0.66020, 0.48204, -0.12508])
theta = 2*theta_orig
C_orig = calcControl(theta_orig, base, den)
C = calcControl(theta, base, den)
L_orig = ctl.series(C_orig, G).minreal()
L = ctl.series(C, G).minreal()
P = ctl.feedback(L).minreal()
P_orig = ctl.feedback(L_orig).minreal()
# step_t = np.arange(0, 50,
#                Ts)
# step_P_y, _ = step(P_orig, step_t)
# step_P_r, _= step(P, step_t)


# plt.plot(step_t, step_P_y)
# plt.plot(step_t, step_P_r)
# plt.show()
# pdb.set_trace()
# exit(-1)
fc = 1/(2*Ts)


omega = np.linspace(0, np.pi/Ts, 500)
mag_orig, _, _=ctl.bode_plot(refModel, omega=omega, color='k',label=r'Reference model $M_r$')
mag_poisoned, _, _ = ctl.bode_plot(P, omega=omega,linestyle='--', color='0.5',label=r'Target model $M_a$')
plt.legend()
plt.savefig(
        'bode_targeted.pdf',
        transparent=True,
        bbox_inches='tight')