MHESettings#

class MHESettings(n_horizon=None, t_step=None, meas_from_data=False, state_discretization='collocation', collocation_type='radau', collocation_deg=2, collocation_ni=1, nl_cons_check_colloc_points=False, nl_cons_single_slack=False, cons_check_colloc_points=True, store_full_solution=False, store_lagr_multiplier=True, store_solver_stats=<factory>, nlpsol_opts=<factory>)[source]#

Bases: EstimatorSettings

Settings for do_mpc.estimator.MHE.

The do_mpc.estimator.MHE automatically creates an instance of type MHESettings and adds it to its class attributes.

Example to change settings:

mhe.settings.n_horizon = 20

Note

Settings cannot be updated after calling do_mpc.estimator.MHE.setup().

Parameters:

  • n_horizon (int) –

  • t_step (float) –

  • meas_from_data (bool) –

  • state_discretization (str) –

  • collocation_type (str) –

  • collocation_deg (int) –

  • collocation_ni (int) –

  • nl_cons_check_colloc_points (bool) –

  • nl_cons_single_slack (bool) –

  • cons_check_colloc_points (bool) –

  • store_full_solution (bool) –

  • store_lagr_multiplier (bool) –

  • store_solver_stats (List[str]) –

  • nlpsol_opts (Dict) –

Methods#

check_for_mandatory_settings#

check_for_mandatory_settings(self)#

Method to assert the necessary settings required to design do_mpc.estimator.MHE

set_linear_solver#

set_linear_solver(self, solver_name='MA27')#

Method to set the linear solver to MA27.

This method enables to set the linear solver to MA27. This change in many cases will drastically boost the speed of do-mpc.

Example:

mhe.settings.set_linear_solver(solver_name = "MA27")
Parameters:

solver_name (str) – Specify the solver name.

supress_ipopt_output#

supress_ipopt_output(self)#

Method to supress the ipopt solver output.

This method set the revelvant settings in the ipopt solver in order to supress the output log.

Attributes#

collocation_deg#

MHESettings.collocation_deg: int = 2#

Choose the collocation degree for continuous models with collocation as state discretization.

collocation_ni#

MHESettings.collocation_ni: int = 1#

For orthogonal collocation, choose the number of finite elements for the states within a time-step (and during constant control input).

Note

Can be used to avoid high-order polynomials.

collocation_type#

MHESettings.collocation_type: str = 'radau'#

Choose the collocation type for continuous models with collocation as state discretization.

Note

Currently only 'radau' collocation type is available.

cons_check_colloc_points#

MHESettings.cons_check_colloc_points: bool = True#

For orthogonal collocation choose whether the linear bounds set with do_mpc.estimator.MHE.bounds are evaluated once per finite Element or for each collocation point.

meas_from_data#

MHESettings.meas_from_data: bool = False#

Default option to retrieve past measurements for the MHE optimization problem.

The do_mpc.estimator.MHE.set_y_fun() is called during setup.

n_horizon#

MHESettings.n_horizon: int = None#

Prediction horizon of the optimal control problem.

Parameter must be set by user.

nl_cons_check_colloc_points#

MHESettings.nl_cons_check_colloc_points: bool = False#

For orthogonal collocation choose wether the bounds set with do_mpc.estimator.MHE.set_nl_cons() are evaluated once per finite Element or for each collocation point.

nl_cons_single_slack#

MHESettings.nl_cons_single_slack: bool = False#

If True, soft-constraints set with do_mpc.estimator.MHE.set_nl_cons() introduce only a single slack variable for the entire horizon.

state_discretization#

MHESettings.state_discretization: str = 'collocation'#

Choose the state discretization for continuous models.

Currently only 'collocation' is available.

Note

This attribute has no effect if model is created in discrete type.

store_full_solution#

MHESettings.store_full_solution: bool = False#

Choose whether to store the full solution of the optimization problem.

This is required for animating the predictions in post processing.

Warning

It drastically increases the required storage.

store_lagr_multiplier#

MHESettings.store_lagr_multiplier: bool = True#

Choose whether to store the lagrange multipliers of the optimization problem.

Warning

Increases the required storage.

t_step#

MHESettings.t_step: float = None#

Timestep of the estimator.

store_solver_stats#

MHESettings.store_solver_stats: List[str]#

Choose which solver statistics to store.

Must be a list of valid statistics.

nlpsol_opts#

MHESettings.nlpsol_opts: Dict#

Dictionary with options for the CasADi solver call nlpsol with plugin ipopt.

All options are listed here.