Source code for do_mpc.controller._controllersettings
#
# This file is part of do-mpc
#
# do-mpc: An environment for the easy, modular and efficient implementation of
# robust nonlinear model predictive control
#
# Copyright (c) 2014-2019 Sergio Lucia, Alexandru Tatulea-Codrean
# TU Dortmund. All rights reserved
#
# do-mpc is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3
# of the License, or (at your option) any later version.
#
# do-mpc is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with do-mpc. If not, see <http://www.gnu.org/licenses/>.
#import data class decorator
from dataclasses import dataclass, field
from typing import Dict,List
@dataclass
class ControllerSettings:
"""Settings for :py:class:`do_mpc.controller`.
This class contains the mandaory settings for the all the controllers available int :py:class:`do_mpc.controller`.
This class creates an instance of type :py:class:`ControllerSettings` and adds it to its class attributes.
"""
t_step: float = None
"""Timestep of the controller"""
def check_for_mandatory_settings(self):
"""Method to assert the necessary settings required to design :py:class:`do_mpc.controller`
"""
if self.t_step is None:
raise ValueError("t_step must be set")
[docs]
@dataclass
class LQRSettings(ControllerSettings):
"""Settings for :py:class:`do_mpc.controller.LQR`.
The :py:class:`do_mpc.controller.LQR` automatically creates an instance of type :py:class:`LQRSettings` and adds it to its class attributes.
Example to change settings:
::
lqr.settings.n_horizon = 20
Note:
Settings cannot be updated after calling :py:meth:`do_mpc.controller.LQR.setup`.
"""
n_horizon: int = None
"""Prediction horizon of the optimal control problem.
Defaults to ``None``, which represents an infinite horizon.
"""
[docs]
@dataclass
class MPCSettings(ControllerSettings):
"""Settings for :py:class:`do_mpc.controller.MPC`.
The :py:class:`do_mpc.controller.MPC` automatically creates an instance of type :py:class:`MPCSettings` and adds it to its class attributes.
Example to change settings:
::
mpc.settings.n_horizon = 20
Note:
Settings cannot be updated after calling :py:meth:`do_mpc.controller.MPC.setup`.
"""
n_horizon:int = None
"""Prediction horizon of the optimal control problem.
Parameter must be set by user"""
n_robust: int = 0
"""Robust horizon for robust scenario-tree MPC.
Note:
Optimization problem grows exponentially with n_robust.
"""
open_loop: bool = False
"""Setting for scenario-tree MPC.
Note:
If the parameter is ``False``, for each timestep AND scenario an individual control input is computed.
If set to ``True``, the same control input is used for each scenario.
"""
use_terminal_bounds: bool = False
"""Choose if terminal bounds for the states are used.
Set terminal bounds with :py:attr:`do_mpc.controller.MPC.terminal_bounds`.
"""
state_discretization: str = 'collocation'
"""Choose the state discretization for continuous models.
Note:
Currently only ``'collocation'`` is available. Defaults to ``'collocation'``.
Has no effect if model is created in discrete type.
"""
collocation_type: str = 'radau'
"""Choose the collocation type for continuous models with collocation as state discretization.
Note:
Currently only ``'radau'`` is available.
"""
collocation_deg: int = 2
"""Choose the collocation degree for continuous models with collocation as state discretization."""
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).
Can be used to avoid high-order polynomials."""
nl_cons_check_colloc_points: bool = False
"""For orthogonal collocation choose whether the nonlinear bounds set with :py:func:`do_mpc.controller.MPC.set_nl_cons` are evaluated once per finite Element or for each collocation point."""
nl_cons_single_slack: bool = False
""" If ``True``, soft-constraints set with :py:func:`do_mpc.controller.MPC.set_nl_cons` introduce only a single slack variable for the entire horizon."""
cons_check_colloc_points: bool = True
"""For orthogonal collocation choose whether the linear bounds set with :py:attr:`do_mpc.controller.MPC.bounds` are evaluated once per finite Element or for each collocation point."""
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. However, it drastically increases the required storage."""
store_lagr_multiplier: bool = True
"""Choose whether to store the lagrange multipliers of the optimization problem.
Note:
Increases the required storage.
"""
store_solver_stats : List[str] = field(default_factory=lambda:['success','t_wall_total'])
"""Choose which solver statistics to store.
Must be a list of valid statistics. This attribute is an object of type list."""
nlpsol_opts: Dict = field(default_factory=dict)
"""Dictionary with options for the CasADi solver call ``nlpsol`` with plugin ``ipopt``.
All options are listed `here <http://casadi.sourceforge.net/api/internal/d4/d89/group__nlpsol.html>`_."""
def check_for_mandatory_settings(self):
"""Method to assert the necessary settings required to design :py:class:`do_mpc.controller.MPC`
"""
super().check_for_mandatory_settings()
if self.n_horizon is None:
raise ValueError("n_horizon must be set")
def 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.
"""
supress_ipopt = {'ipopt.print_level':0, 'ipopt.sb': 'yes', 'print_time':0}
self.nlpsol_opts.update(supress_ipopt)
def set_linear_solver(self,solver_name: str = "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:
::
mpc.settings.set_linear_solver(solver_name = "MA27")
Args:
solver_name: Specify the linear solver name
"""
self.nlpsol_opts['ipopt.linear_solver'] = solver_name
