def test_syn_ss_sol_simulate(self):
"""Verifies that dyn_ss_sol mathes a simulation"""
for roll in np.linspace(math.radians(-20), math.radians(20), num=11):
for u in np.linspace(1, 30, num=10):
A, B = create_dyn_state_matrices(u, self.VM)
# Convert to discrete time system
ss = StateSpace(A, B, np.eye(2), np.zeros((2, 2)))
ss = ss.sample(0.01)
for sa in np.linspace(math.radians(-20), math.radians(20), num=11):
inp = np.array([[sa], [roll]])
# Simulate for 1 second
x1 = np.zeros((2, 1))
for _ in range(100):
x1 = ss.A @ x1 + ss.B @ inp
# Compute steady state solution directly
x2 = dyn_ss_sol(sa, u, roll, self.VM)
np.testing.assert_almost_equal(x1, x2, decimal=3)
#Velocidade Real
df = pd.read_csv('file.csv')
velocidadeReal = df['Velocidade'].tolist()
#modelo do sistema
A = np.array([[-R / L, -K / L], [K / J, -b / J]])
B = np.array([[1 / L, 0], [0, -1 / J]])
C = np.array([0, 1])
D = np.array([0, 0])
sys = StateSpace(A, B, C, D)
#discretizando o modelo
dsys = sys.sample(dt)
#Setando os dados que serao usados
Samples = len(velocidadeReal)
tempo = np.arange(0, (Samples + 1) * dt, dt)
P = np.array([[0.1389, 0], [0, 0.0389]])
#Sendo Q a covariancia do ruido do preocesso. O que representa a incerteza do modelo
Q = np.array([[0.1, 0], [0, 0.1]])
#Sendo R ruido na medicao
R = [0.1]
#Imagina-se os estados iniciais como 0
