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 run_system_simulation(self, x0, time_points, udata = None, \
integrator_options = {}, print_status = True):
r'''
:param x0: state values :math:`x_0 \in \mathbb{R}^{\text{n}_\text{x}}`
at the first time point :math:`t_0`
:type x0: numpy.ndarray, casadi.DMatrix, list
:param time_points: switching time points for the controls
:math:`t_\text{N} \in \mathbb{R}^\text{N}`
:type time_points: numpy.ndarray, casadi.DMatrix, list
:param udata: optional, values for the time-varying controls at the
first :math:`N-1` switching time points
:math:`u_\text{N} \in \mathbb{R}^{\text{n}_\text{u} \times \text{N}-1}`; if no values
are given, 0 will be used
:type udata: numpy.ndarray, casadi.DMatrix
:param integrator_options: optional, options to be passed to the CasADi
integrator (see the CasADi documentation
for a list of all possible options)
:type integrator_options: dict
:param print_status: optional, set to ``True`` (default) or ``False`` to
enable or disable console printing.
:type print_status: bool
This function will run a system simulation for the specified initial
state values and control data from :math:`t_0` to
:math:`t_\text{N}`.
If you receive integrator-related error messages during the simulation,
please check the corresponding parts of the
CasADi documentation.
After the simulation has finished, the simulation results
:math:`x_\text{N}` can be accessed via the class attribute
``Simulation.simulation_results``.
'''
if print_status:
print('\n' + '# ' + 23 * '-' + \
' casiopeia system simulation ' + 22 * '-' + ' #')
print('\nRunning system simulation, this might take some time ...')
self.__initialize_simulation(x0 = x0, time_points = time_points, \
udata = udata, integrator_options_user = integrator_options)
self.__simulation_results = ci.horzcat([ \
self.__x0,
self.__simulation(x0 = self.__x0, p = self.__simulation_input)["xf"]
])
if print_status:
print("\nSystem simulation finished.")
def run_system_simulation(self, x0, time_points, udata=None, integrator_options={}, print_status=True):
r"""
:param x0: state values :math:`x_0 \in \mathbb{R}^{\text{n}_\text{x}}`
at the first time point :math:`t_0`
:type x0: numpy.ndarray, casadi.DMatrix, list
:param time_points: switching time points for the controls
:math:`t_\text{N} \in \mathbb{R}^\text{N}`
:type time_points: numpy.ndarray, casadi.DMatrix, list
:param udata: optional, values for the time-varying controls at the
first :math:`N-1` switching time points
:math:`u_\text{N} \in \mathbb{R}^{\text{n}_\text{u} \times \text{N}-1}`; if no values
are given, 0 will be used
:type udata: numpy.ndarray, casadi.DMatrix
:param integrator_options: optional, options to be passed to the CasADi
integrator (see the CasADi documentation
for a list of all possible options)
:type integrator_options: dict
:param print_status: optional, set to ``True`` (default) or ``False`` to
enable or disable console printing.
:type print_status: bool
This function will run a system simulation for the specified initial
state values and control data from :math:`t_0` to
:math:`t_\text{N}`.
If you receive integrator-related error messages during the simulation,
please check the corresponding parts of the
CasADi documentation.
After the simulation has finished, the simulation results
:math:`x_\text{N}` can be accessed via the class attribute
``Simulation.simulation_results``.
"""
if print_status:
print("\n" + "# " + 23 * "-" + " casiopeia system simulation " + 22 * "-" + " #")
print("\nRunning system simulation, this might take some time ...")
self.__initialize_simulation(
x0=x0, time_points=time_points, udata=udata, integrator_options_user=integrator_options
)
self.__simulation_results = ci.horzcat(
[self.__x0, self.__simulation(x0=self.__x0, p=self.__simulation_input)["xf"]]
)
if print_status:
print("\nSystem simulation finished.")
def _set_optimization_variables_initials(self, qinit, x0, uinit):
self.simulation = Simulation(self._discretization.system, \
self._pdata, qinit)
self.simulation.run_system_simulation(x0, \
self._discretization.time_points, uinit, print_status = False)
xinit = self.simulation.simulation_results
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],
])
uinit = inputchecks.check_controls_data(uinit, \
self._discretization.system.nu, \
self._discretization.number_of_intervals)
Uinit = uinit
qinit = inputchecks.check_constant_controls_data(qinit, \
self._discretization.system.nq)
Qinit = qinit
self._optimization_variables_initials = ci.veccat([ \
Uinit,
Qinit,
Xinit,
])
def _set_optimization_variables_initials(self, qinit, x0, uinit):
self.simulation = Simulation(self._discretization.system, \
self._pdata, qinit)
self.simulation.run_system_simulation(x0, \
self._discretization.time_points, uinit, print_status = False)
xinit = self.simulation.simulation_results
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],
])
uinit = inputchecks.check_controls_data(uinit, \
self._discretization.system.nu, \
self._discretization.number_of_intervals)
Uinit = uinit
qinit = inputchecks.check_constant_controls_data(qinit, \
self._discretization.system.nq)
Qinit = qinit
self._optimization_variables_initials = ci.veccat([ \
Uinit,
Qinit,
Xinit,
])
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,
])
