.. _sphx_glr_auto_examples_optimal_control:

.. _optimal_control_examples:

Optimal control examples
------------------------

These examples detail how to use :mod:`csnlp` to create and solve optimal control
problems with :class:`csnlp.wrappers.Mpc`, :class:`csnlp.wrappers.ScenarioBasedMpc`, and
other similar wrapper classes.



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This example demos how an MPC controller can be built via csnlp.wrappers.Mpc and how it can be solved in an open-loop fashion.">

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  .. image:: /auto_examples/optimal_control/images/thumb/sphx_glr_open_loop_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_optimal_control_open_loop.py`

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      <div class="sphx-glr-thumbnail-title">Simple open-loop MPC controller</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demos how an MPC controller can be built for a linear system using csnlp.wrappers.Mpc and its csnlp.wrappers.Mpc.set_affine_dynamics method. The system is a simple linear system with the following dynamics:">

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  .. image:: /auto_examples/optimal_control/images/thumb/sphx_glr_linear_mpc_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_optimal_control_linear_mpc.py`

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      <div class="sphx-glr-thumbnail-title">Linear MPC control</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the csnlp.wrappers.MultiScenarioMpc to create a multi-scenario MPC controller. We will only solve it once to demonstrate its open-loop solution, but of course the controller can and should be used in a receding horizon fashion, as done in the other examples. It is highly recommended to first go through the example scenario_based_mpc_example to familiarize yourself with csnlp.wrappers.ScenarioBasedMpc.">

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  .. image:: /auto_examples/optimal_control/images/thumb/sphx_glr_mpc_multiple_scenarios_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_optimal_control_mpc_multiple_scenarios.py`

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      <div class="sphx-glr-thumbnail-title">Multiple Scenario MPC</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we demonstrate how to build a scenario-based model predictive control (SCMPC) using csnlp.wrappers.ScenarioBasedMpc. The problem is inspired by the numerical results of schildbach_scenario_2014 shown in Tables 1 and 2, for R = R_1 = R_2 = 0.">

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  .. image:: /auto_examples/optimal_control/images/thumb/sphx_glr_scenario_approach_for_mpc_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_optimal_control_scenario_approach_for_mpc.py`

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      <div class="sphx-glr-thumbnail-title">Scenario-based MPC</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demos how an MPC controller can be built for a simple system with piecewise affine dynamics using csnlp.wrappers.PwaMpc. We assume some knowledge of MPC is already present; otherwise, refer to other optimal control examples. For more details on the subject of control for PWA and hybrid systems , see bemporad_control_1999 and borrelli_predictive_2017.">

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  .. image:: /auto_examples/optimal_control/images/thumb/sphx_glr_mpc_for_pwa_system_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_optimal_control_mpc_for_pwa_system.py`

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      <div class="sphx-glr-thumbnail-title">MPC controller for PWA systems</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/optimal_control/open_loop
   /auto_examples/optimal_control/linear_mpc
   /auto_examples/optimal_control/mpc_multiple_scenarios
   /auto_examples/optimal_control/scenario_approach_for_mpc
   /auto_examples/optimal_control/mpc_for_pwa_system