def _merge_optimization_variables(self):
optimization_variables = [ \
pe_setup._optimization_variables for \
pe_setup in self._pe_setups]
self._optimization_variables = ci.vertcat(optimization_variables)
def _merge_optimization_variables(self):
optimization_variables = [ \
doe_setup._optimization_variables for \
doe_setup in self._doe_setups]
self._optimization_variables = ci.vertcat(optimization_variables)
def _setup_constraints(self):
self._constraints = ci.vertcat([ \
self._measurement_deviations,
self._discretization.equality_constraints,
])
def _merge_cov_matrix_derivative_directions(self):
cov_matrix_derivative_directions = \
[doe_setup._cov_matrix_derivative_directions for
doe_setup in self._doe_setups]
self._cov_matrix_derivative_directions = \
ci.vertcat(cov_matrix_derivative_directions)
def _merge_optimization_variables_upper_bounds(self):
optimization_variables_upper_bounds = [ \
doe_setup._optimization_variables_upper_bounds for \
doe_setup in self._doe_setups]
self._optimization_variables_upper_bounds = \
ci.vertcat(optimization_variables_upper_bounds)
def _merge_optimization_variables_initials(self):
optimization_variables_initials = [ \
doe_setup._optimization_variables_initials for \
doe_setup in self._doe_setups]
self._optimization_variables_initials = \
ci.vertcat(optimization_variables_initials)
def _merge_optimization_variables_initials(self):
optimization_variables_initials = [ \
pe_setup._optimization_variables_initials for \
pe_setup in self._pe_setups]
self._optimization_variables_initials = \
ci.vertcat(optimization_variables_initials)
def _merge_cov_matrix_derivative_directions(self):
cov_matrix_derivative_directions = \
[doe_setup._cov_matrix_derivative_directions for
doe_setup in self._doe_setups]
self._cov_matrix_derivative_directions = \
ci.vertcat(cov_matrix_derivative_directions)
def _merge_optimization_variables_upper_bounds(self):
optimization_variables_upper_bounds = [ \
doe_setup._optimization_variables_upper_bounds for \
doe_setup in self._doe_setups]
self._optimization_variables_upper_bounds = \
ci.vertcat(optimization_variables_upper_bounds)
def _setup_constraints(self):
self._constraints = ci.vertcat([ \
self._measurement_deviations,
self._equality_constraints_controls_applied,
])
def _setup_constraints(self):
self._constraints = ci.vertcat([ \
self._measurement_deviations,
self._discretization.equality_constraints,
])
def _setup_constraints(self):
self._constraints = ci.vertcat([ \
self._measurement_deviations,
self._equality_constraints_controls_applied,
])
def _setup_gauss_newton_lagrangian_hessian(self):
gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
ci.mx(self._cov_matrix_derivative_directions.shape[0] - \
self._weightings_vectorized.shape[0], 1), \
self._weightings_vectorized])
self._gauss_newton_lagrangian_hessian = ci.diag( \
gauss_newton_lagrangian_hessian_diag)
def _set_measurement_deviations(self):
self._measurement_deviations = ci.vertcat([ \
ci.vec(self._measurements_controls_applied) - \
self._measurement_data_vectorized + \
ci.vec(self._discretization.optimization_variables["V"])
])
def _set_measurement_deviations(self):
self._measurement_deviations = ci.vertcat([ \
ci.vec(self._discretization.measurements) - \
self._measurement_data_vectorized + \
ci.vec(self._discretization.optimization_variables["V"])
])
def _setup_gauss_newton_lagrangian_hessian(self):
gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
ci.mx(self._cov_matrix_derivative_directions.shape[0] - \
self._weightings_vectorized.shape[0], 1), \
self._weightings_vectorized])
self._gauss_newton_lagrangian_hessian = ci.diag( \
gauss_newton_lagrangian_hessian_diag)
def __generate_scaled_dae(self):
# ODE time scaling according to:
# https://groups.google.com/forum/#!topic/casadi-users/AeXzJmBH0-Y
t_scale = ci.mx_sym("t_scale", 1)
self.__dae_scaled = {"x": self.__system.x, \
"p": ci.vertcat([t_scale, self.__system.u]), \
"ode": t_scale * self.__ode_parameters_applied}
def gauss_newton_lagrangian_hessian(self):
gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
ci.mx(self._optimization_variables.shape[0] - \
self._weightings_vectorized.shape[0], 1), \
self._weightings_vectorized])
gauss_newton_lagrangian_hessian = ci.diag( \
gauss_newton_lagrangian_hessian_diag)
return gauss_newton_lagrangian_hessian
def gauss_newton_lagrangian_hessian(self):
gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
ci.mx(self._optimization_variables.shape[0] - \
self._weightings_vectorized.shape[0], 1), \
self._weightings_vectorized])
gauss_newton_lagrangian_hessian = ci.diag( \
gauss_newton_lagrangian_hessian_diag)
return gauss_newton_lagrangian_hessian
def _setup_covariance_matrix(self, kkt_matrix, \
number_of_unknown_parameters):
I = ci.mx_eye(number_of_unknown_parameters)
O = ci.mx(kkt_matrix.shape[0] - number_of_unknown_parameters, \
number_of_unknown_parameters)
Z_p = ci.vertcat([I, O])
self._covariance_matrix = ci.solve(kkt_matrix, Z_p, "csparse")[ \
:number_of_unknown_parameters, :number_of_unknown_parameters]
def _setup_covariance_matrix(self, kkt_matrix, \
number_of_unknown_parameters):
I = ci.mx_eye(number_of_unknown_parameters)
O = ci.mx(kkt_matrix.shape[0] - number_of_unknown_parameters, \
number_of_unknown_parameters)
Z_p = ci.vertcat([I,O])
self._covariance_matrix = ci.solve(kkt_matrix, Z_p, "csparse")[ \
:number_of_unknown_parameters, :number_of_unknown_parameters]
def __generate_scaled_dae(self):
# ODE time scaling according to:
# https://groups.google.com/forum/#!topic/casadi-users/AeXzJmBH0-Y
t_scale = ci.mx_sym("t_scale", 1)
dae_scaled = ci.mx_function(
"dae_scaled",
ci.daeIn(x=self.__system.x, p=ci.vertcat([t_scale, self.__system.u])),
ci.daeOut(ode=t_scale * self.__ode_parameters_applied),
)
self.__dae_scaled = dae_scaled.expand()
def _merge_and_setup_constraints(self):
constraints = [self._doe_setups[0]._constraints]
for k, doe_setup in enumerate(self._doe_setups[:-1]):
# Connect estimation problems by setting p_k - p_k+1 = 0
constraints.append(doe_setup._discretization.optimization_variables["P"] - \
self._doe_setups[k+1]._discretization.optimization_variables["P"])
constraints.append(self._doe_setups[k+1]._constraints)
self._constraints = ci.vertcat(constraints)
def _merge_and_setup_constraints(self):
constraints = [self._pe_setups[0]._constraints]
for k, pe_setup in enumerate(self._pe_setups[:-1]):
# Connect estimation problems by setting p_k - p_k+1 = 0
constraints.append(pe_setup._discretization.optimization_variables["P"] - \
self._pe_setups[k+1]._discretization.optimization_variables["P"])
constraints.append(self._pe_setups[k + 1]._constraints)
self._constraints = ci.vertcat(constraints)
def __initialize_simulation(self, x0, time_points, udata, integrator_options_user):
self.__x0 = inputchecks.check_states_data(x0, self.__system.nx, 0)
time_points = inputchecks.check_time_points_input(time_points)
number_of_integration_steps = time_points.size - 1
time_steps = time_points[1:] - time_points[:-1]
udata = inputchecks.check_controls_data(udata, self.__system.nu, number_of_integration_steps)
self.__simulation_input = ci.vertcat([np.atleast_2d(time_steps), udata])
integrator_options = integrator_options_user.copy()
integrator_options.update({"t0": 0, "tf": 1}) # , "number_of_finite_elements": 1})
# integrator = ci.Integrator("integrator", "rk", \
integrator = ci.Integrator("integrator", "cvodes", self.__dae_scaled, integrator_options)
self.__simulation = integrator.mapaccum("simulation", number_of_integration_steps)
def __initialize_simulation(self, x0, time_points, udata, \
integrator_options_user):
self.__x0 = inputchecks.check_states_data(x0, self.__system.nx, 0)
time_points = inputchecks.check_time_points_input(time_points)
number_of_integration_steps = time_points.size - 1
time_steps = time_points[1:] - time_points[:-1]
udata = inputchecks.check_controls_data(udata, self.__system.nu, \
number_of_integration_steps)
self.__simulation_input = ci.vertcat([np.atleast_2d(time_steps), udata])
integrator_options = integrator_options_user.copy()
integrator_options.update({"t0": 0, "tf": 1, "expand": True}) # , "number_of_finite_elements": 1})
# integrator = ci.Integrator("integrator", "rk", \
integrator = ci.Integrator("integrator", "cvodes", \
self.__dae_scaled, integrator_options)
self.__simulation = integrator.mapaccum("simulation", \
number_of_integration_steps)
def _merge_measurement_data(self):
measurement_data = [pe_setup._measurement_data_vectorized for \
pe_setup in self._pe_setups]
self._measurement_data_vectorized = ci.vertcat(measurement_data)
def _merge_weightings(self):
weightings = [pe_setup._weightings_vectorized for \
pe_setup in self._pe_setups]
self._weightings_vectorized = ci.vertcat(weightings)
def _merge_residuals(self):
residuals = [pe_setup._residuals for \
pe_setup in self._pe_setups]
self._residuals = ci.vertcat(residuals)
def _merge_measurement_data(self):
measurement_data = [pe_setup._measurement_data_vectorized for \
pe_setup in self._pe_setups]
self._measurement_data_vectorized = ci.vertcat(measurement_data)
def _merge_weightings(self):
weightings = [pe_setup._weightings_vectorized for \
pe_setup in self._pe_setups]
self._weightings_vectorized = ci.vertcat(weightings)
def _merge_residuals(self):
residuals = [pe_setup._residuals for \
pe_setup in self._pe_setups]
self._residuals = ci.vertcat(residuals)
