MPCData¶

class do_mpc.data.MPCData(model)[source]¶

do-mpc data container for the do_mpc.controller.MPC instance. This method inherits from Data and extends it to query the MPC predictions.

__getitem__(ind)¶

Query data fields. This method can be used to obtain the stored results in the Data instance.

The full list of available fields can be inspected with:

print(data.data_fields)

The dict also denotes the dimension of each field.

The method allows for power indexing the results for the fields _x, _u, _z, _tvp, _p, _aux, _y where further indices refer to the configured variables in the do_mpc.model.Model instance.

Example:

# Assume the following model was used (excerpt):
model = do_mpc.model.Model('continuous')

model.set_variable('_x', 'Temperature', shape=(5,1)) # Vector
model.set_variable('_p', 'disturbance', shape=(3,3)) # Matrix
model.set_variable('_u', 'heating')                  # scalar

...

# the model was used (among others) for the MPC controller
mpc = do_mpc.controller.MPC(model)

...

# Query the mpc.data instance:
mpc.data['_x']                      # Return all states
mpc.data['_x', 'Temperature']       # Return the 5 temp states
mpc.data['_x', 'Temperature', :2]   # Return the first 2 temp. states
mpc.data['_p', 'disturbance', 0, 2] # Matrix allows for further indices

# Other fields can also be queried, e.g.:
mpc.data['_time']                   # current time
mpc.data['t_wall_S']               # optimizer runtime
# These do not allow further indices.

Returns:	Returns the queried data field (for all time instances)
Return type:	numpy.ndarray

Methods

`MPCData.export`	The export method returns a dictionary of the stored data.
`MPCData.init_storage`	Create new (empty) arrays for all variables.
`MPCData.prediction`	Query the MPC trajectories.
`MPCData.set_meta`	Set meta data for the current instance of the data object.
`MPCData.update`	Update value(s) of the data structure with key word arguments.

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