Class method.

do_mpc.optimizer.Optimizer.set_tvp_fun(self, tvp_fun)

Set function which returns time-varying parameters.

The tvp_fun is called at each optimization step to get the current prediction of the time-varying parameters. The supplied function must be callable with the current time as the only input. Furthermore, the function must return a CasADi structured object which is based on the horizon and on the model definition. The structure can be obtained with get_tvp_template().


# in model definition:
alpha = model.set_variable(var_type='_tvp', var_name='alpha')
beta = model.set_variable(var_type='_tvp', var_name='beta')

# in optimizer configuration:
tvp_temp_1 = optimizer.get_tvp_template()
tvp_temp_1['_tvp', :] = np.array([1,1])

tvp_temp_2 = optimizer.get_tvp_template()
tvp_temp_2['_tvp', :] = np.array([0,0])

def tvp_fun(t_now):
    if t_now<10:
        return tvp_temp_1



The method set_tvp_fun(). must be called prior to setup IF time-varying parameters are defined in the model. It is not required to call the method if no time-varying parameters are defined.

Parameters:tvp_fun (function) – Function that returns the predicted tvp values at each timestep. Must have single input (float) and return a structure3.DMStruct (obtained with get_tvp_template()).

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