Source code for do_mpc.estimator._ekf

#
#   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 numpy as np
from ._base import Estimator




class EKF(Estimator):
    """Extended Kalman Filter. Setup this class and use :py:func:`EKF.make_step`
    during runtime to obtain the currently estimated states given the measurements ``y0``.

    Warnings:
        Not currently implemented.
    """
    def __init__(self, model):
        raise Exception('EKF is not currently supported. This is a placeholder.')
        super().__init__(model)

        # Flags are checked when calling .setup.
        self.flags = {
            'setup': False,
        }

    def make_step(self, y0):
        """Main method during runtime. Pass the most recent measurement and
        retrieve the estimated state."""
        assert self.flags['setup'] == True, 'EKF was not setup yet. Please call EKF.setup().'
        None