Release notes

This content is autogenereated from our Github release notes.

do-mpc v4.0.0

We are finally out of beta with do-mpc v4.0.0. Thanks to everyone who has contributed, for the feedback and all the interest. This release includes some important changes and bugfixes and also significantly extends our homepage do-mpc.

We hope you will like the new features and content. Development will now continue with work on version 4.1.0 (and potentially some in between versions with minor features). Stay tuned on our Github page and feel free to open issues or join the discussion!

Major changes

New properties for Simulator, Estimator and MPC

Inheriting from the new class IteratedVariables these classes now obtain the attributes _x0, _u0, _z0 (and _p_est0). Users can access these attributes with the properties with x0, u0, z0 (and p_est0), which are listed in the documentation and have sanity checks etc. when setting them. This fixes e.g. #55. These new properties are used for two things:

Set initial values

For the simulator the initial state is self explanatory and a very important attribute. For the MHE and MPC class the attributes are used when calling the important set_initial_guess method, which does exactly that: Set the initial guess of the optimization problem.

Obtain the current values of the iterated variables

This is very useful for conditional MPC loops: E.g. stop the controller and simulation when a certain state has reached a certain value.

Measurement noise

Currently, the do_mpc.model.Model.set_rhs method allows to set an additive process noise. This is used for the MHE optimization problem. In a similar fashion, the do_mpc.model.Model.set_meas method now allows to set an additive measurement noise.

In the MHE the measurement noise is introduced as a new optimization variable and the measurement equation is added as an additional constraint. The full optimization problem now looks like this: image This change makes it possible for the user to decide, which measurements are enforced and which can be perturbed. A typical example would be to ensure that input “measurements” are completely trusted.

Simulator with disturbances

The newly introduced measurement noise and the existing process noise are now used within the simulator. With each call of Simulator.make_step values can be passed to obtain an imperfectly simulated and measured system..

Documentation

• Release notes are now included in the documentation. They are autogenerated from the Github release notes which can be accessed via Rest API.
• The release notes are appended with a section that includes a download link for the example files that were written for the respective versions.
• Installation instructions now refer to these download links. This solves #62 .
• Added new section Example gallery, explaining the supplied examples in do-mpc in Jupyter Notebooks (rendered on readthedocs)
• Added new section Background with various articles explaining the mathematics behind do-mpc.
• Parameter collocation_ni in MPC/MHE is now explained more clearly.

Minor changes

• Renamed model.setup_model() -> model.setup()in all examples. This adresses #38
• opt_p_num and opt_x_num for MHE/MPC are now instance properties instead of class attributes. They still appear in the documentation and can be used as before. Having them as class attributes can lead to problems when multiple classes are live during the same session.

Example files

Please download the example files for release do-mpc v4.0.0 here.

do-mpc v4.0.0-beta3

Major changes

Data

• New __getitem__ method to conveniently retrieve values from Data object (details here)
• New MPCData class (which inherits form Data). This adds the prediction method, which can be used to query the optimal trajectories. Details here.

Both methods were previously (in a slightly different form) in the Graphics module. They are still used in this class but can also be convenient under different circumstances.

Graphics

The Graphics module is now initialized with a specific Data instance (e.g. mpc.data). Each Data class has their own Graphics class (if it is supposed to be displayed). Compared to the previous implementation, we now initialize all lines that are supposed to be plotted (and store them in pred_lines and result_lines). During runtime, the data on these lines is getting updated.

• Added new structure class in do_mpc.tools. Used for tracking the new Graphics properties: pred_lines and result_lines.
• The properties pred_lines and result_lines can be used to retrieve line instances with power indices. Line instances can be easily configured (linestyle, alpha, color etc.)

Process noise

Process noise can be added to rhs of Model class: link

This is solving issue #53 .

This change was necessary to allow for the more natural MHE formulation where the process noise is penalised in the cost function. The user can define for each state (vector) individually if this is intended or not.

As a consequence of this change I had to introduce the new variable w throughout do-mpc. For the MPC and simulator module this is without effect.

The main difference is here

Remark: The change also allows to estimate parameters that change over time (e.g. environmental influences). Our regular estimated parameters are constant over the entire MHE horizon, which is not always valid. To estimate varying parameters, they should be defined as states with unknown dynamics. Concretely, their RHS is zero (for ODEs) and they have a high process noise.

Symbolic variables for MHE weighting matrices

As originally intended, it is now possible to have symbolic matrices as MHE tuning factors. The result of this change can be seen in the rotating_oscillating_masses example.

The symbolic variables are defined in the do-mpc Model where typically, you want to have P_x and P_p as parameters and P_y and P_w as time-varying parameters. Example of their definition.

and here they are used.

The purpose of using symbolic weighting is of course to update them at each iteration. Since they are parameters and time-varying parameters respectively, this is done with the set_p_fun and set_tvp_fun method of the MHE: link

Note that in the example above, we don’t actually need varying weighting matrices and the returned values are in fact constant. This can be seen as a proof of concept.

This change had some other implications. Most notably, having additional parameters interferes with the multi-stage robust MPC module. Where we previously had to pass a number of scenarios for each defined parameter. Since parameters for the MHE are irrelevant for MPC the API for the call set_uncertainty_values has changed: link

The new API is fully backwards compatible. However, it is much more intuitive now. The function is called with keyword arguments, where each keyword refers to one uncertain parameter (note that we can ignore the parameters that are irrelevant). In practice this looks something like this

Example files

Please download the example files for release do-mpc v4.0.0-beta3 here.

do-mpc v4.0.0-beta2

Error in release. Immediately replaced with beta3.

do-mpc v4.0.0-beta1

Major changes

• We are now explicitly pointing out attributes of the Model such as states, inputs, etc. These should be used to obtain these attributes and replace the previous get_variables method which is now depreciated. The Model also supports a __get_variable__ call now to conveniently select items.
• setup_model is replaced by setup to be more consistent with other setup methods. The old method is still available and shows a depreciation warning.
• The MHE now supports the set_default_objective method.

Bug fixes

• The MHE formulation had an error in the make_step method. We used the wrong time step from the previous solution to compute the arrival cost.

Other changes

• Spelling in documentation
• New guide about installing HSL linear solver
• Credits in documentation

Example files

Please download the example files for release do-mpc v4.0.0-beta1 here.

do-mpc v4.0.0-beta

do-mpc has undergone a massive overhaul and comes with a completely new interface, new features and a comprehensive documentation.

Please note that previously written code is not compatible with do-mpc 4.0.0. If you want to continue working with older code please use version 3.0.0.

This is the beta release of version 4.0.0. We expect minor modifications and bug fixes in the near future.

Please see our documentation on our new project homepage www.do-mpc.com for a full list of features.

Example files

Please download the example files for release do-mpc v4.0.0-beta here.

do-mpc v3.0.0

Main modifications

• Support for CasADi version 3.4.4
• Support for time-varying parameters
• Support for discrete-time systems

do-mpc v2.0.0

Compatible with CasADi 3.0.0

do-mpc version 1.0.0