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force_feedback.py
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import numpy as np
import matplotlib.pyplot as plt
from utils.params import *
from scipy.integrate import odeint
# Constructing M, C and H matrices
M = (m_foot * l_foot**2)/3
C = Bh+Br
H = Br + Bh + C
# list to store values
theta_list = [theta]
theta_dot_list = [theta_dot]
alpha_list = [1]
kr_list = []
kh_list = []
tau_r_list = []
theta_ddot_list = [0]
theta_e_list = []
Emax = -np.inf
# function to calculate theta desired
def calc_theta_d(t,mov):
if t<=2:
tht = 0.244346*(20*t**3 - 15*t**4 + 3*t**5)/16 - 0.122173
else:
tht = 0.122173
if mov == 1:
return tht
else:
return -tht
# ODE solver function
def adaptive_AIC(y, t, kr_bar, tau_e, M, C, H, m_foot, l_foot_com, mov, alpha, theta_adm ):
theta_d = calc_theta_d(t, mov)
theta_e = theta_d - y[0]
theta_e_list.append(theta_e)
G = -m_foot * 9.81 *l_foot_com * np.cos(y[0])
noise = np.random.randint(0, 1)
kh = noise + np.exp(np.log(101)/3*t) - 0.5
k_rstar = 1/(beta*(1+kf)**2*kh) - kf*kh/(1+kf)
k_rc = (G-tau_e)/theta_adm
k_ra = min(k_rc, k_rstar)
kr_bar = alpha*k_ra
tau_h = kh*theta_e - Bh * y[1]
# Applying Force feedback approach
tau_r = (kr_bar*(theta_e) - Br* y[1] + tau_e) + kf*((kr_bar*(theta_e) - Br* y[1] + tau_e) + tau_h)
theta_ddot = (1/M)*(tau_r + tau_h - C*y[1] - G)
theta_ddot_list.append(theta_ddot)
return [y[1], theta_ddot]
# specify the number of movements
n_movements = 2
for movement in range(n_movements):
E = 0
t = np.linspace(0, 3, 300)
if movement % 2 == 0:
# going up
mov = 1
tau_e1 = -1*tau_e
theta_adm1 = theta_adm
else:
# going down
mov =2
theta_adm1 = -theta_adm
tau_e1 = tau_e
sol = odeint(adaptive_AIC, [theta_list[-1], theta_dot_list[-1]], t, args=(kr_bar, tau_e1, M, C, H, m_foot, l_foot_com, mov, alpha_list[-1], theta_adm1))
# print(sol)
theta_list += list(sol[:,0])
theta_dot_list += list(sol[:,1])
# update alpha:
E = sum(theta_e_list)
theta_e_list=[]
Emax = max(Emax, E)
P = E/Emax
alpha = f*alpha_list[-1] + g*P
alpha_list.append(alpha)
for i in range(len(list(sol[:,0]))):
theta_desired = calc_theta_d(i/100,mov)
theta_e = theta_desired - sol[i,0]
G = -m_foot * 9.81 *l_foot_com * np.cos(sol[i,0])
noise = np.random.randint(0, 1)
kh = noise + np.exp(np.log(101)/3*i/100) - 0.5
kh_list.append(kh)
k_rstar = 1/(beta*(1+kf)**2*kh) - kf*kh/(1+kf)
k_rc = (G-tau_e1)/theta_adm1
k_ra = min(k_rc, k_rstar)
kr_bar = alpha*k_ra
kr_list.append(kr_bar)
tau_h = kh*theta_e - Bh * sol[i,1]
tau_r = (kr_bar*(theta_e) - Br* sol[i,1] + tau_e) + kf*((kr_bar*(theta_e) - Br* sol[i,1] + tau_e1) + tau_h)
tau_r_list.append(tau_r)
theta_d = []
for movement in range(n_movements):
if movement % 2 == 0:
# going up
mov = 1
else:
# going down
mov =2
theta_d += [calc_theta_d(q,mov) for q in t]
fig, ax = plt.subplots(2, 2)
ax[0][0].plot(theta_d, label = 'desired theta')
ax[0][0].plot(theta_list, label = 'theta')
ax[0][0].set_xlabel('Time')
ax[0][0].set_ylabel('Theta')
ax[0][0].legend(loc = 'upper right')
ax[0][1].plot(kr_list)
ax[0][1].set_xlabel('Time')
ax[0][1].set_ylabel('Kr')
ax[1][0].plot(tau_r_list)
ax[1][0].set_xlabel('Time')
ax[1][0].set_ylabel('Tau_r')
ax[1][1].plot(kh_list)
ax[1][1].set_xlabel('Time')
ax[1][1].set_ylabel('Kh')
fig.suptitle('Force Feedback - Movement 2')
plt.show()