def furuta_dfo_cost(x):
# We need to scale the inputs x down since they are scaled up
# versions of the parameters (x = scalefactor*[param1 param2])
armFrictionCoefficient = x[0] / 1e3
pendulumFrictionCoefficient = x[1] / 1e3
model = load_fmu(
os.path.join(curr_dir, '..', 'examples', 'files', 'FMUs',
'Furuta.fmu'))
# Set new parameter values into the model
model.set('armFriction', armFrictionCoefficient)
model.set('pendulumFriction', pendulumFrictionCoefficient)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 50
# Simulate model response with new parameter values
res = model.simulate(start_time=0., final_time=40, options=opts)
# Load simulation result
phi_sim = res['armJoint.phi']
theta_sim = res['pendulumJoint.phi']
t_sim = res['time']
# Evaluate the objective function
y_meas = N.vstack((phi_meas, theta_meas))
y_sim = N.vstack((phi_sim, theta_sim))
obj = dfo.quad_err(t_meas, y_meas, t_sim, y_sim)
return obj
def f1(x):
model = load_fmu(fmu_name)
# We need to scale the inputs x down since they are scaled up
# versions of a1 and a2 (x = scalefactor*[a1 a2])
a1 = x[0] / 1e6
a2 = x[1] / 1e6
# Set new values for a1 and a2 into the model
model.set('qt.a1', a1)
model.set('qt.a2', a2)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 0
# Simulate model response with new parameters a1 and a2
res = model.simulate(input=(['u1', 'u2'], u),
start_time=0.,
final_time=60,
options=opts)
# Load simulation result
x1_sim = res['qt.x1']
x2_sim = res['qt.x2']
t_sim = res['time']
# Evaluate the objective function
y_meas = N.vstack((y1_meas, y2_meas))
y_sim = N.vstack((x1_sim, x2_sim))
obj = dfo.quad_err(t_meas, y_meas, t_sim, y_sim)
return obj
def furuta_dfo_cost(x):
# We need to scale the inputs x down since they are scaled up
# versions of the parameters (x = scalefactor*[param1 param2])
armFrictionCoefficient = x[0]/1e3
pendulumFrictionCoefficient = x[1]/1e3
model = load_fmu(os.path.join(curr_dir, '..', 'examples', 'files', 'FMUs', 'Furuta.fmu'))
# Set new parameter values into the model
model.set('armFriction', armFrictionCoefficient)
model.set('pendulumFriction', pendulumFrictionCoefficient)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 0
# Simulate model response with new parameter values
res = model.simulate(start_time=0., final_time=40, options=opts)
# Load simulation result
phi_sim = res['armJoint.phi']
theta_sim = res['pendulumJoint.phi']
t_sim = res['time']
# Evaluate the objective function
y_meas = N.vstack((phi_meas, theta_meas))
y_sim = N.vstack((phi_sim, theta_sim))
obj = dfo.quad_err(t_meas, y_meas, t_sim, y_sim)
return obj
def f1(x):
model = load_fmu(fmu_name)
# We need to scale the inputs x down since they are scaled up
# versions of a1 and a2 (x = scalefactor*[a1 a2])
a1 = x[0]/1e6
a2 = x[1]/1e6
# Set new values for a1 and a2 into the model
model.set('qt.a1',a1)
model.set('qt.a2',a2)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 0
# Simulate model response with new parameters a1 and a2
res = model.simulate(input=(['u1','u2'],u),start_time=0.,final_time=60,options=opts)
# Load simulation result
x1_sim = res['qt.x1']
x2_sim = res['qt.x2']
t_sim = res['time']
# Evaluate the objective function
y_meas = N.vstack((y1_meas,y2_meas))
y_sim = N.vstack((x1_sim,x2_sim))
obj = dfo.quad_err(t_meas,y_meas,t_sim,y_sim)
return obj
def f2(x):
model = load_fmu(fmu_name)
# We need to scale the inputs x down since they are scaled up
# versions of a1, a2, a3 and a4 (x = scalefactor*[a1 a2 a3 a4])
a1 = x[0] / 1e6
a2 = x[1] / 1e6
a3 = x[2] / 1e6
a4 = x[3] / 1e6
# Set new values for a1, a2, a3 and a4 into the model
model.set('qt.a1', a1)
model.set('qt.a2', a2)
model.set('qt.a3', a3)
model.set('qt.a4', a4)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 0
# Simulate model response with the new parameters
res = model.simulate(input=(['u1', 'u2'], u),
start_time=0.,
final_time=60,
options=opts)
# Load simulation result
x1_sim = res['qt.x1']
x2_sim = res['qt.x2']
x3_sim = res['qt.x3']
x4_sim = res['qt.x4']
t_sim = res['time']
# Evaluate the objective function
y_meas = [y1_meas, y2_meas, y3_meas, y4_meas]
y_sim = [x1_sim, x2_sim, x3_sim, x4_sim]
obj = dfo.quad_err(t_meas, y_meas, t_sim, y_sim)
return obj
def f2(x):
model = load_fmu(fmu_name)
# We need to scale the inputs x down since they are scaled up
# versions of a1, a2, a3 and a4 (x = scalefactor*[a1 a2 a3 a4])
a1 = x[0]/1e6
a2 = x[1]/1e6
a3 = x[2]/1e6
a4 = x[3]/1e6
# Set new values for a1, a2, a3 and a4 into the model
model.set('qt.a1',a1)
model.set('qt.a2',a2)
model.set('qt.a3',a3)
model.set('qt.a4',a4)
# Create options object and set verbosity to zero to disable printouts
opts = model.simulate_options()
opts['CVode_options']['verbosity'] = 0
# Simulate model response with the new parameters
res = model.simulate(input=(['u1','u2'],u),start_time=0.,final_time=60,options=opts)
# Load simulation result
x1_sim = res['qt.x1']
x2_sim = res['qt.x2']
x3_sim = res['qt.x3']
x4_sim = res['qt.x4']
t_sim = res['time']
# Evaluate the objective function
y_meas = [y1_meas,y2_meas,y3_meas,y4_meas]
y_sim = [x1_sim,x2_sim,x3_sim,x4_sim]
obj = dfo.quad_err(t_meas,y_meas,t_sim,y_sim)
return obj
