from Linear import Linear
from L import calcular_L
from sympy import *
# Define global parameters
A_1, A_c, A_2, K_1, K_c, K_2, b_c = var("A_1 A_c A_2 K_1 K_c K_2 b_c")
# Create a linear object
sys = Linear()
# Define the sizes of the state and the input
x = sys.state(3)
u = sys.input(1)
# Define functions that can't be defined as functions above, i.e.
# f(y,x_1,x_2,x_3,...,x_n) = g(y,x_1,x_2,x_3,...,x_n)
# The method receive the name of the function, the f(y,x_1,x_2,...,x_n)
# function, the g(y,x_1,x_2,...,x_n) function, and a list with tuples
# representing the args that call the function (this is a hack,
# because the actual matching system is not as good as i want).
# State function
sys.f(Matrix(
[u[0]/A_1 -K_1 / A_1 * pow(x[0], 2.475),
(K_1 / A_c) * pow(x[0], 2.475) - (K_c * b_c / A_c) * pow(x[1], 1.8),
(K_c * b_c / A_2) * pow(x[1], 1.8) - (K_2 / A_2) * x[2]
])
)
# Output function
def L(beta):
return L_k + 4 * r * ( beta - tan(beta)) + l / cos(beta)
def T(rho):
return Y * A * (rho_o /rho - 1)
def rho(L,M):
return M /(A * L)
# Create a linear object
sys = Linear()
# Define the sizes of the state and the input
x = sys.state(3)
u = sys.input(2)
# Define functions that can't be defined as functions above, i.e.
# f(y,x_1,x_2,x_3,...,x_n) = g(y,x_1,x_2,x_3,...,x_n)
# The method receive the name of the function, the f(y,x_1,x_2,...,x_n)
# function, the g(y,x_1,x_2,...,x_n) function, and a list with tuples
# representing the args that call the function (this is a hack,
# because the actual matching system is not as good as i want).
beta = sys.function(
"beta",
lambda y,x: l * sin(y) - 4*r, # f(y, x_1, x_2, x_3. ... , x_n)
lambda y,x: 2 * x * cos(y), # g(y, x_1, x_2, x_3, ... , x_n)
[(x[0],)]
)
