Class method.

do_mpc.model.Model.set_variable(self, var_type, var_name, shape=(1, 1))

Introduce new variables to the model class. Define variable type, name and shape (optional).


# States struct (optimization variables):
C_a = model.set_variable(var_type='_x', var_name='C_a', shape=(1,1))
T_K = model.set_variable(var_type='_x', var_name='T_K', shape=(1,1))

# Input struct (optimization variables):
Q_dot = model.set_variable(var_type='_u', var_name='Q_dot')

# Fixed parameters:
alpha = model.set_variable(var_type='_p', var_name='alpha')


var_type allows a shorthand notation e.g. _x which is equivalent to states.

  • var_type (string) –

    Declare the type of the variable. The following types are valid (long or short name is possible):

    Long name short name Remark
    states _x Required
    inputs _u optional
    algebraic _z Optional
    parameter _p Optional
    timevarying_parameter _tvp Optional
  • var_name – Set a user-defined name for the parameter. The names are reused throughout do_mpc.
  • shape (int or tuple of length 2.) – Shape of the current variable (optional), defaults to 1.
  • assertion – var_type must be string
  • assertion – var_name must be string
  • assertion – shape must be tuple or int
  • assertion – Cannot call after setup().

Returns the newly created symbolic variable.

Return type:


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