.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "drive_examples/current_vector/plot_6kw_pmsyrm_sat_cvc.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_drive_examples_current_vector_plot_6kw_pmsyrm_sat_cvc.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_drive_examples_current_vector_plot_6kw_pmsyrm_sat_cvc.py:


5.6-kW saturated PM-SyRM, CVC
=============================

This example simulates sensorless current-vector control (CVC) of the same 5.6-kW
permanent-magnet synchronous reluctance machine (PM-SyRM) as the corresponding flux-
vector control example. See :doc:`/drive_examples/flux_vector/plot_6kw_pmsyrm_sat_fvc`
for the machine model, flux-map data, and saturation-model description. This example
demonstrates online reference generation in current-vector control, which allows the
optimal current references to be generated in real time [#Sar2026]_.

.. GENERATED FROM PYTHON SOURCE LINES 14-22

.. code-block:: Python


    from pathlib import Path

    import numpy as np

    import motulator.drive.control.sm as control
    from motulator.drive import model, utils








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Compute base values based on the nominal values.

.. GENERATED FROM PYTHON SOURCE LINES 24-28

.. code-block:: Python


    nom = utils.NominalValues(U=460, I=8.8, f=60, P=5.6e3, tau=29.7)
    base = utils.BaseValues.from_nominal(nom, n_p=2)








.. GENERATED FROM PYTHON SOURCE LINES 29-30

Configure the system model.

.. GENERATED FROM PYTHON SOURCE LINES 30-50

.. code-block:: Python


    # Load the measured flux map data used to parametrize the machine model
    p = Path(utils.__file__).resolve().parents[3] / "examples/drive/data"
    meas_data = np.load(p / "baldor_400rpm_map.npz")
    i_s_dq_map = meas_data["i_s_dq"]
    psi_s_dq_map = meas_data["psi_s_dq"]

    # Create the flux map from the measured data and invert it to get the current map
    meas_flux_map = utils.MagneticModel(
        i_s_dq=i_s_dq_map, psi_s_dq=psi_s_dq_map, type="flux_map"
    )
    meas_curr_map = meas_flux_map.invert()

    # Create the drive model
    par = model.SaturatedSynchronousMachinePars(n_p=2, R_s=0.63, i_s_dq_fcn=meas_curr_map)
    machine = model.SynchronousMachine(par)
    mechanics = model.MechanicalSystem(J=0.05)
    converter = model.VoltageSourceConverter(u_dc=540)
    mdl = model.Drive(machine, mechanics, converter)








.. GENERATED FROM PYTHON SOURCE LINES 51-52

Configure the control system.

.. GENERATED FROM PYTHON SOURCE LINES 52-87

.. code-block:: Python


    # Create a saturation model for the controller
    est_current_map = utils.SaturationModelPMSyRM(
        a_d0=3.96,
        a_dd=28.5,
        S=4,
        a_q0=1.1 * 5.89,  # Unsaturated q-axis inductance is underestimated for robustness
        a_qq=2.67,
        T=6,
        a_dq=41.5,
        U=1,
        V=1,
        a_b=81.75,
        a_bp=1,
        k_q=0.1,
        psi_n=0.804,
        W=2,
    )

    # Generate the estimated flux map
    est_current_map = est_current_map.as_magnetic_model(
        d_range=np.linspace(-0.1 * base.psi, base.psi, 256),
        q_range=np.linspace(-1.4 * base.psi, 1.4 * base.psi, 256),
    )
    est_flux_map = est_current_map.invert()
    est_par = control.SaturatedSynchronousMachinePars(
        n_p=2, R_s=0.63, psi_s_dq_fcn=est_flux_map
    )
    cfg = control.CurrentVectorControllerCfg(
        i_s_max=2 * base.i, J=0.05, sensorless=True, online_ref=True
    )
    vector_ctrl = control.CurrentVectorController(est_par, cfg)
    speed_ctrl = control.SpeedController(J=0.05, alpha_s=2 * np.pi * 4)
    ctrl = control.VectorControlSystem(vector_ctrl, speed_ctrl)








.. GENERATED FROM PYTHON SOURCE LINES 88-89

Set the speed reference and the external load torque.

.. GENERATED FROM PYTHON SOURCE LINES 89-93

.. code-block:: Python


    ctrl.set_speed_ref(lambda t: (t > 0.2) * 2 * base.w_M)
    mdl.mechanics.set_external_load_torque(lambda t: (t > 1.25) * 0.7 * nom.tau)








.. GENERATED FROM PYTHON SOURCE LINES 94-95

Create the simulation object, simulate, and plot the results in per-unit values.

.. GENERATED FROM PYTHON SOURCE LINES 95-100

.. code-block:: Python


    sim = model.Simulation(mdl, ctrl)
    res = sim.simulate(t_stop=2)
    utils.plot(res, base)




.. image-sg:: /drive_examples/current_vector/images/sphx_glr_plot_6kw_pmsyrm_sat_cvc_001.png
   :alt: plot 6kw pmsyrm sat cvc
   :srcset: /drive_examples/current_vector/images/sphx_glr_plot_6kw_pmsyrm_sat_cvc_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 101-106

.. rubric:: References

.. [#Sar2026] Sarén, Hartikainen, Piippo, Hinkkanen, "Decoupled online feedforward
   generation of optimal references for saturated synchronous machine drives,
   2026, https://arxiv.org/abs/2607.08528


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 14.735 seconds)


.. _sphx_glr_download_drive_examples_current_vector_plot_6kw_pmsyrm_sat_cvc.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_6kw_pmsyrm_sat_cvc.ipynb <plot_6kw_pmsyrm_sat_cvc.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_6kw_pmsyrm_sat_cvc.py <plot_6kw_pmsyrm_sat_cvc.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_6kw_pmsyrm_sat_cvc.zip <plot_6kw_pmsyrm_sat_cvc.zip>`


.. only:: html

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