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First of all we should create a stochastic formulation without considering the GP as in the paper.
Create new class called something like GaussianRandomVariable to define the stochastic variable. This should contain mean and standard deviation
Allow to add (later to multiply) this variable with the model.
The SMPC class should detect the different parts (random variables and non) to be able to do the necessary operations.
Note
At the moment one can sum the GP to the RHS of the model using the '+' operator. This adds the mean of the GP to the model. Think about using another operator to add the mean, while keeping the '+' operator to define a stochastic model.
The text was updated successfully, but these errors were encountered:
Add simple stochastic MPC as in this paper.
Main idea
First of all we should create a stochastic formulation without considering the GP as in the paper.
GaussianRandomVariable
to define the stochastic variable. This should contain mean and standard deviationNote
At the moment one can sum the GP to the RHS of the model using the '+' operator. This adds the mean of the GP to the model. Think about using another operator to add the mean, while keeping the '+' operator to define a stochastic model.
The text was updated successfully, but these errors were encountered: