get_p_template¶
Class method.
-
do_mpc.controller.MPC.
get_p_template
(self, n_combinations)¶ Obtain output template for
set_p_fun()
.Low level API method to set user defined scenarios for robust multi-stage MPC by defining an arbitrary number of combinations for the parameters defined in the model. For more details on robust multi-stage MPC please read our background article.
The method returns a structured object which is initialized with all zeros. Use this object to define values of the parameters for an arbitrary number of scenarios (defined by
n_combinations
).This structure (with numerical values) should be used as the output of the
p_fun
function which is set to the class withset_p_fun()
.Use the combination of
get_p_template()
andset_p_template()
as a more adaptable alternative toset_uncertainty_values()
.Note
We advice less experienced users to use
set_uncertainty_values()
as an alterntive way to configure the scenario-tree for robust multi-stage MPC.Example:
# in model definition: alpha = model.set_variable(var_type='_p', var_name='alpha') beta = model.set_variable(var_type='_p', var_name='beta') ... # in MPC configuration: n_combinations = 3 p_template = MPC.get_p_template(n_combinations) p_template['_p',0] = np.array([1,1]) p_template['_p',1] = np.array([0.9, 1.1]) p_template['_p',2] = np.array([1.1, 0.9]) def p_fun(t_now): return p_template MPC.set_p_fun(p_fun)
Note the nominal case is now: alpha = 1 beta = 1 which is determined by the order in the arrays above (first element is nominal).
Parameters: n_combinations (int) – Define the number of combinations for the uncertain parameters for robust MPC. Returns: None Return type: None
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