set_param¶
Class method.

do_mpc.estimator.MHE.
set_param
(self, **kwargs)¶ Method to set the parameters of the
MHE
class. Parameters must be passed as pairs of valid keywords and respective argument. For example:mhe.set_param(n_horizon = 20)
It is also possible and convenient to pass a dictionary with multiple parameters simultaneously as shown in the following example:
setup_mhe = { 'n_horizon': 20, 't_step': 0.5, } mhe.set_param(**setup_mhe)
This makes use of thy python “unpack” operator. See more details here.
Note
The only required parameters are
n_horizon
andt_step
. All other parameters are optional.Note
set_param()
can be called multiple times. Previously passed arguments are overwritten by successive calls.The following parameters are available:
Parameters:  n_horizon (int) – Prediction horizon of the optimal control problem. Parameter must be set by user.
 t_step (float) – Timestep of the mhe.
 meas_from_data (bool) – Default option to retrieve past measurements for the MHE optimization problem. The
set_y_fun()
is called during setup.  state_discretization (str) – Choose the state discretization for continuous models. Currently only
'collocation'
is available. Defaults to'collocation'
. Has no effect if model is created indiscrete
type.  collocation_type (str) – Choose the collocation type for continuous models with collocation as state discretization. Currently only
'radau'
is available. Defaults to'radau'
.  collocation_deg (int) – Choose the collocation degree for continuous models with collocation as state discretization. Defaults to
2
.  collocation_ni (int) – For orthogonal collocation, choose the number of finite elements for the states within a timestep (and during constant control input). Defaults to
1
. Can be used to avoid highorder polynomials.  nl_cons_check_colloc_points (bool) – For orthogonal collocation choose wether the bounds set with
set_nl_cons()
are evaluated once per finite Element or for each collocation point. Defaults toFalse
(once per collocation point).  cons_check_colloc_points (bool) – For orthogonal collocation choose whether the linear bounds set with
bounds
are evaluated once per finite Element or for each collocation point. Defaults toTrue
(for all collocation points).  nl_cons_single_slack (bool) – If
True
, softconstraints set withset_nl_cons()
introduce only a single slack variable for the entire horizon. Defaults toFalse
.  store_full_solution (bool) – Choose whether to store the full solution of the optimization problem. This is required for animating the predictions in post processing. However, it drastically increases the required storage. Defaults to False.
 store_lagr_multiplier (bool) – Choose whether to store the lagrange multipliers of the optimization problem. Increases the required storage. Defaults to
True
.  store_solver_stats (dict) – Choose which solver statistics to store. Must be a list of valid statistics. Defaults to
['success','t_wall_S']
.  nlpsol_opts – Dictionary with options for the CasADi solver call
nlpsol
with pluginipopt
. All options are listed here.
Note
We highly suggest to change the linear solver for IPOPT from mumps to MA27. In many cases this will drastically boost the speed of dompc. Change the linear solver with:
optimizer.set_param(nlpsol_opts = {'ipopt.linear_solver': 'MA27'})
Note
To suppress the output of IPOPT, please use:
suppress_ipopt = {'ipopt.print_level':0, 'ipopt.sb': 'yes', 'print_time':0} optimizer.set_param(nlpsol_opts = suppress_ipopt)
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