v4.6.4

Release notes

Please see the notes of release v4.6.3.

v4.6.3

Minor changes

• The penalty term (rterm) of the MPC formulation can now be customized. For more information, see the documentation
• All CasADI integrator options can now be accessed via the simulator.settings syntax. For more information see the documentation
• The settings attribute of the MPC and Simulator classes is now called _settings. It can be accessed via the settings method. The user interface remains identical.
• Minor bug fixes in the documentation an the Simulator class

v4.6.2

Minor changes

• Added new settings attribute in _mpc.py
• Added citation file

v4.6.1

Minor changes

• Fixed #387
• do-mpc can now be installed either as:

pip install do_mpc

or

pip install do_mpc[full]

Only with the full installation, optional features are available, e.g.:

• ONNX conversion
• OPCUA

Backend changes

• Fixed readthedosc configuration according to the new specification
• New setup for requirement files (splitting the pip requirements files according to this description):
  • requirements.txt lists the base requirements for the do-mpc core features.
  • requirements_full.txt is a superset of requirements and includes optional do-mpc features (e.g. ONNX conversion).
  • requirements_docs.txt is a superset of the full requirements and includes the packages required to build the docs.

v4.6.0

Major changes

For detailed information on the major changes, please visit our website www.do-mpc.com.

OPC UA module

Building on the opcua-asnycio library, do-mpc now facilitates plant communication and software-in-the-loop testing with OPC UA.

• Core MPC modules can automatically derive and OPC UA client
• A local OPC UA server can be configured with namespace derived from clients for testing purposes

Interoperability with deep learning toolboxes through ONNX

do-mpc now supports the open neural network exchange (ONNX) standard.

• Incorporate neural networks previously trained in Tensorflow, Pytorch, Matlab, etc. (with an option to export ONNX models)
• Convert ONNX models to CasADi expressions (the backbone of do-mpc).

Improved interface for settings in the MPC, MHE, Simulator, etc.

All do-mpc core modules now have the important new attribute settings.
Previously, settings were passed to set_param. This method is still available and wraps the new interface. The new method has important advantages:

1. The settings attribute can be printed to see the current configuration.
2. Context help is available in most IDEs (e.g. VS Code) to see the available settings, the type and a description.

Relaunch of documentation

We have significantly improved our documentation with a polished new look and included typing information for an improved workflow.

v4.5.1

Please see the changes for v.4.5.0. This is a minor update to fix the installation routine of do-mpc.

v4.5.0

Major changes

Linear control

• Newly implemented class LinearModel. The linear model can be created by:
  • linearizing a regular (nonlinear) Model instance
  • passing system matrices (A,B,C,D)
  • creating (linear) equations in LinearModel with the well known syntax used in Model.
• Discretize a (continuous-time) LinearModel.
• Setup the new discrete-time linear quadratic regulator (LQR) controller class

Data-based system identification with neural networks

• Use neural networks as system models in do-mpc
  • ONNXConversion can convert a previously trained and stored neural network to a CasADi graph
  • ONNX files can be exported from most major neural network frameworks (e.g. tensorflow, pytorch, matlab, ...)
  • Some limitations to the neural network operations apply.

Minor changes

Compile NLP

• The MHE, MPC classes can now export and compile the NLP.
• Compiled NLPs can be loaded and solved. This may result in faster optimization times.

Improved sampling tools for data generation

• Optional parameters in __init__ of Sampler, SamplingPlanner, DataHandler that are passed to set_param. This allows for a more concise setup.
• SamplingPlanner method product to create cartesian product (grid) of input variables to create test cases.

Simulator

• Simulator.make_step() can now be called without control input for autonomous systems.

Bug fixes

• Example files now import do-mpc relative path with os.path.join to yield a OS agnostic implementation.
• MHE class can now be created without estimating parameters.
• Solves visualization bug described in #340

Backend

• Significant code refactoring
  • Many modules (e.g. controller.py) were getting to large
  • Individual files (e.g. _mpc.py) in subfolder do_mpc/controller/ for large classes
  • User-facing classes (e.g. MPC) imported in do_mpc/controller/__init__.py.
    • Imported such that do_mpc.controller.MPC still yields the MPC class
    • No changes in front-end for users
  • Recursive documentation with Sphinx / Readthedocs is fully automated now. Important considerations:
    • Private files (marked as e.g. _mpc.py) are not documented
    • Imported modules are documented (e.g. the imported MPC class.
    • Importing with from casadi import * could result in documentation of CasADi package in do-mpc. This is avoided by:
      • Explicitly excluding certain packages (e.g. casadi) to be documented.
      • Marking functions or classes as private with _Name.
      • Using the __all__ = [...] variable to mark in certain files the list of elements that should be documented.

v4.4.0

Major changes

• MHE/MPC bounds on optimiziation variables can now be changed after calling mhe.setup() and mpc.setup() respectively (fixes #289). The simplest way to set bounds is the mhe.bounds and mpc.bounds property (docs)
• More granular control over the bounds is now possible, e.g. choosing different values for each time-step of the horizon or for different collocation points (if that makes sense). For this purpose two now properties lb_opt_x and ub_opt_x are now documented and accessible to the user. These properties are indexed similarly to the property opt_x. Importantly, setting new values on these structure automatically considers the scaling factors.
• The do-mpc model can now be pickled. Pickling is restricted, however and requires (error messages are thrown otherwise):
  • the model class must be setup
  • the model must use SX symbolic variables
• Enhanced warmstarting: The solver is now supplied with a guess for the dual variables

Minor changes

• Bug fix: MPCData.prediction was previously unable to query algebraic states _z.
• Fixed #283: Algebraic states can now be plotted with the graphics package

v4.3.5

Minor fixes

• Setup for release in v4.3.4 was incomplete.

v4.3.4

Minor fixes

• model.aux can now be queried before calling model.setup()
• Some typos in documentation