Model conversions

pynamics.models.model_conversion

This module contains helper functions that make Pynamics compatible with the Python Control Systems Library.

Functions:

Name Description
pynamics_to_control

Convert a pynamics linear state-space model to a control state-space model.
control_to_pynamics

Convert a control state-space model to a pynamics state-space model.

control_to_pynamics(control_model, initial_state, initial_control, input_labels=None, output_labels=None)

Convert a control linear state-space model to a pynamics linear model.

This method can be used to convert a control model to a pynamics model, thus making the two libraries compatible.

Parameters:

Name Type Description Default
control_model ss

A Python Control Systems library linear state-space model.

 required
initial_state ndarray

The system's initial state. Should be an array shaped (n, 1), where n is the number of variables.

 required
initial_control ndarray

The inputs' initial value(s). Should be an array shaped (u, 1), where u is the number of input variables.

 required
input_labels list[str] | None

Names of the input variables. Should be a list of length n.

 None
output_labels list[str] | None

Names of the output variables. Should be a list of length u.

 None

Returns:

Type Description
LinearModel

A pynamics linear model.

Examples:

>>> import numpy as np
>>> import control as ct
>>> from pynamics.models.model_conversion import control_to_pynamics
>>>
>>> A = np.array([[0, 0, -1], [1, 0, -3], [0, 1, -3]]);
>>> B = np.array([1, -5, 1]).reshape(-1, 1);
>>> C = np.array([0, 0, 1]);
>>> D = np.array([0]);
>>> ct_model = ct.ss(A, B, C, D);
>>>
>>> py_model = control_to_pynamics(ct_model, initial_state=np.zeros((3, 1)), initial_control=np.zeros((1, 1)));
>>> isinstance(py_model, pynamics.models.state_space_models.LinearModel)
True
>>>
>>> py_model.A
array([[ 0.,  0., -1.],
       [ 1.,  0., -3.],
       [ 0.,  1., -3.]])
>>>
>>> py_model.B
array([[ 1.],
       [-5.],
       [ 1.]])
>>>
>>> py_model.C
array([[0., 0., 1.]])
>>>
>>> py_model.D
array([[0.]])
Source code in pynamics/models/model_conversion.py 
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def control_to_pynamics(control_model: ct.ss, 
                        initial_state: np.ndarray, 
                        initial_control: np.ndarray,
                        input_labels: list[str] | None=None, 
                        output_labels: list[str] | None=None) -> LinearModel:
    """
    Convert a control linear state-space model to a pynamics linear model.

    This method can be used to convert a control model to a pynamics model, thus making the two libraries compatible.

    Parameters
    ----------
    control_model : ct.ss
        A Python Control Systems library linear state-space model.

    initial_state : np.ndarray
        The system's initial state. Should be an array shaped (n, 1), where
        n is the number of variables.

    initial_control : np.ndarray
        The inputs' initial value(s). Should be an array shaped (u, 1), where
        u is the number of input variables.

    input_labels : list[str] | None, optional
        Names of the input variables. Should be a list of length n.

    output_labels : list[str] | None, optional
        Names of the output variables. Should be a list of length u.

    Returns
    -------
    LinearModel
        A pynamics linear model.

    Examples
    --------
    >>> import numpy as np
    >>> import control as ct
    >>> from pynamics.models.model_conversion import control_to_pynamics
    >>>
    >>> A = np.array([[0, 0, -1], [1, 0, -3], [0, 1, -3]]);
    >>> B = np.array([1, -5, 1]).reshape(-1, 1);
    >>> C = np.array([0, 0, 1]);
    >>> D = np.array([0]);
    >>> ct_model = ct.ss(A, B, C, D);
    >>>
    >>> py_model = control_to_pynamics(ct_model, initial_state=np.zeros((3, 1)), initial_control=np.zeros((1, 1)));
    >>> isinstance(py_model, pynamics.models.state_space_models.LinearModel)
    True
    >>>
    >>> py_model.A
    array([[ 0.,  0., -1.],
           [ 1.,  0., -3.],
           [ 0.,  1., -3.]])
    >>>
    >>> py_model.B
    array([[ 1.],
           [-5.],
           [ 1.]])
    >>>
    >>> py_model.C
    array([[0., 0., 1.]])
    >>>
    >>> py_model.D
    array([[0.]])
    """

    return LinearModel(initial_state, 
                       initial_control, 
                       control_model.A, 
                       control_model.B, 
                       control_model.C, 
                       control_model.D, 
                       input_labels, 
                       output_labels);

pynamics_to_control(pynamics_model)

Convert a pynamics linear model to a control linear state-space model.

This method can be used to convert a pynamics model to a control model, thus making the two libraries compatible.

Parameters:

Name Type Description Default
pynamics_model LinearModel

A pynamics linear state-space model.

 required

Returns:

Type Description
ss

A state-space model compatible with the Python Control Systems library.

Examples:

>>> import numpy as np
>>> import control as ct
>>> from pynamics.models.state_space_models import LinearModel
>>> from pynamics.models.model_conversion import pynamics_to_control
>>>
>>> A = np.array([[0, 0, -1], [1, 0, -3], [0, 1, -3]]);
>>> B = np.array([1, -5, 1]).reshape(-1, 1);
>>> C = np.array([0, 0, 1]);
>>> D = np.array([0]);
>>> py_model = LinearModel(np.zeros((3, 1)), np.zeros((1, 1)), A, B, C, D);
>>>
>>> ct_model = pynamics_to_control(py_model);
>>> isinstance(ct_model, ct.statesp.StateSpace)
True
>>>
>>> ct_model.A
array([[ 0.,  0., -1.],
       [ 1.,  0., -3.],
       [ 0.,  1., -3.]])
>>>
>>> ct_model.B
array([[ 1.],
       [-5.],
       [ 1.]])
>>>
>>> ct_model.C
array([[0., 0., 1.]])
>>>
>>> ct_model.D
array([[0.]])
Source code in pynamics/models/model_conversion.py 
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def pynamics_to_control(pynamics_model: LinearModel) -> ct.ss:
    """
    Convert a pynamics linear model to a control linear state-space model.

    This method can be used to convert a pynamics model to a control model, thus making the two libraries compatible.

    Parameters
    ----------
    pynamics_model : LinearModel
        A pynamics linear state-space model.

    Returns
    -------
    ct.ss
        A state-space model compatible with the Python Control Systems library.

    Examples
    --------
    >>> import numpy as np
    >>> import control as ct
    >>> from pynamics.models.state_space_models import LinearModel
    >>> from pynamics.models.model_conversion import pynamics_to_control
    >>>
    >>> A = np.array([[0, 0, -1], [1, 0, -3], [0, 1, -3]]);
    >>> B = np.array([1, -5, 1]).reshape(-1, 1);
    >>> C = np.array([0, 0, 1]);
    >>> D = np.array([0]);
    >>> py_model = LinearModel(np.zeros((3, 1)), np.zeros((1, 1)), A, B, C, D);
    >>>
    >>> ct_model = pynamics_to_control(py_model);
    >>> isinstance(ct_model, ct.statesp.StateSpace)
    True
    >>>
    >>> ct_model.A
    array([[ 0.,  0., -1.],
           [ 1.,  0., -3.],
           [ 0.,  1., -3.]])
    >>>
    >>> ct_model.B
    array([[ 1.],
           [-5.],
           [ 1.]])
    >>>
    >>> ct_model.C
    array([[0., 0., 1.]])
    >>>
    >>> ct_model.D
    array([[0.]])
    """

    return ct.ss(pynamics_model.A, pynamics_model.B, pynamics_model.C, pynamics_model.D);