Class method., ind, t_ind=-1)

Query the MPC trajectories. Use this method to obtain specific MPC trajectories from the data object.


This method requires that the optimal solution is stored in the instance. Storing the optimal solution must be activated with do_mpc.controller.MPC.set_param().

Querying predicted trajectories requires the use of power indices, which is passed as tuple e.g.:

data.prediction((var_type, var_name, i), t_ind)


  • var_type refers to _x, _u, _z, _tvp, _p, _aux
  • var_name refers to the user-defined names in the do_mpc.model.Model
  • Use i to index vector valued variables.

The method returns a multidimensional numpy.ndarray. The dimensions refer to:

arr = data.prediction(('_x', 'x_1'))
>> (n_size, n_horizon, n_scenario)


  • n_size denoting the number of elements in x_1, where n_size = 1 is a scalar variable.
  • n_horizon is the MPC horizon defined with do_mpc.controller.MPC.set_param()
  • n_scenario refers to the number of uncertain scenarios (for robust MPC).

Additional to the power index tuple, a time index (t_ind) can be passed to access the prediction for a certain time.

Parameters:ind (tuple) – Power index to query the prediction of a specific variable.
Returns:Predicted trajectories for the queries variable.
Return type:numpy.ndarray

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