def _set_optimization_variables_initials(self, pinit, xinit):
xinit = inputchecks.check_states_data(xinit, \
self._discretization.system.nx, \
self._discretization.number_of_intervals)
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.size() / self._discretization.system.nx)),
xinit[:, -1],
])
pinit = inputchecks.check_parameter_data(pinit, \
self._discretization.system.np)
Pinit = pinit
Vinit = np.zeros(self._discretization.optimization_variables["V"].shape)
EPS_Uinit = np.zeros( \
self._discretization.optimization_variables["EPS_U"].shape)
self._optimization_variables_initials = ci.veccat([ \
Pinit,
Xinit,
EPS_Uinit,
Vinit,
])
def _set_optimization_variables_lower_bounds(self, umin, qmin, xmin, x0):
umin_user_provided = umin
umin = inputchecks.check_controls_data(umin, \
self._discretization.system.nu, 1)
if umin_user_provided is None:
umin = -np.inf * np.ones(umin.shape)
Umin = ci.repmat(umin, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmin_user_provided = qmin
qmin = inputchecks.check_constant_controls_data(qmin, \
self._discretization.system.nq)
if qmin_user_provided is None:
qmin = -np.inf * np.ones(qmin.shape)
Qmin = qmin
xmin_user_provided = xmin
xmin = inputchecks.check_states_data(xmin, \
self._discretization.system.nx, 0)
if xmin_user_provided is None:
xmin = -np.inf * np.ones(xmin.shape)
Xmin = ci.repmat(xmin, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmin[:,0] = x0
self._optimization_variables_lower_bounds = ci.veccat([ \
Umin,
Qmin,
Xmin,
])
def _set_optimization_variables_lower_bounds(self, umin, qmin, xmin, x0):
umin_user_provided = umin
umin = inputchecks.check_controls_data(umin, \
self._discretization.system.nu, 1)
if umin_user_provided is None:
umin = -np.inf * np.ones(umin.shape)
Umin = ci.repmat(umin, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmin_user_provided = qmin
qmin = inputchecks.check_constant_controls_data(qmin, \
self._discretization.system.nq)
if qmin_user_provided is None:
qmin = -np.inf * np.ones(qmin.shape)
Qmin = qmin
xmin_user_provided = xmin
xmin = inputchecks.check_states_data(xmin, \
self._discretization.system.nx, 0)
if xmin_user_provided is None:
xmin = -np.inf * np.ones(xmin.shape)
Xmin = ci.repmat(xmin, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmin[:,0] = x0
self._optimization_variables_lower_bounds = ci.veccat([ \
Umin,
Qmin,
Xmin,
])
def _set_optimization_variables_upper_bounds(self, umax, qmax, xmax, x0):
umax_user_provided = umax
umax = inputchecks.check_controls_data(umax, \
self._discretization.system.nu, 1)
if umax_user_provided is None:
umax = np.inf * np.ones(umax.shape)
Umax = ci.repmat(umax, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmax_user_provided = qmax
qmax = inputchecks.check_constant_controls_data(qmax, \
self._discretization.system.nq)
if qmax_user_provided is None:
qmax = -np.inf * np.ones(qmax.shape)
Qmax = qmax
xmax_user_provided = xmax
xmax = inputchecks.check_states_data(xmax, \
self._discretization.system.nx, 0)
if xmax_user_provided is None:
xmax = np.inf * np.ones(xmax.shape)
Xmax = ci.repmat(xmax, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmax[:,0] = x0
self._optimization_variables_upper_bounds = ci.veccat([ \
Umax,
Xmax,
])
def _set_optimization_variables_upper_bounds(self, umax, qmax, xmax, x0):
umax_user_provided = umax
umax = inputchecks.check_controls_data(umax, \
self._discretization.system.nu, 1)
if umax_user_provided is None:
umax = np.inf * np.ones(umax.shape)
Umax = ci.repmat(umax, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmax_user_provided = qmax
qmax = inputchecks.check_constant_controls_data(qmax, \
self._discretization.system.nq)
if qmax_user_provided is None:
qmax = -np.inf * np.ones(qmax.shape)
Qmax = qmax
xmax_user_provided = xmax
xmax = inputchecks.check_states_data(xmax, \
self._discretization.system.nx, 0)
if xmax_user_provided is None:
xmax = np.inf * np.ones(xmax.shape)
Xmax = ci.repmat(xmax, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmax[:,0] = x0
self._optimization_variables_upper_bounds = ci.veccat([ \
Umax,
Xmax,
])
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 _set_optimization_variables_initials(self, pinit, xinit):
xinit = inputchecks.check_states_data(xinit, \
self._discretization.system.nx, \
self._discretization.number_of_intervals)
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.numel() / self._discretization.system.nx)),
xinit[:, -1],
])
pinit = inputchecks.check_parameter_data(pinit, \
self._discretization.system.np)
Pinit = pinit
Vinit = np.zeros(
self._discretization.optimization_variables["V"].shape)
EPS_Uinit = np.zeros( \
self._discretization.optimization_variables["EPS_U"].shape)
self._optimization_variables_initials = ci.veccat([ \
Pinit,
Xinit,
Vinit,
EPS_Uinit,
])
