Towards enhanced weight selection for (N)MPC via multi-objective optimisation

Having the possibility to systematically evaluate objectives of different nature at the same time is becoming crucial to operate processes, plants and production sites under more sustainable conditions. Most often, this multi-objective nature is tackled by combining all individual objectives into a global Weighted Sum (WS). This approach is widespread because it is easy to use and understand, but it also suffer of intrinsic drawbacks. This research makes use of advanced multi-objective methods that mitigate these drawbacks and introduce a novel procedure systematically linking a solution obtain with advanced methods to a set of weights for a WS objective function. Appling this set of weights leads the WS to generate the same exact solution obtain with advanced methods. This procedure is particularly appealing for the systematic tuning of (Nonlinear) Model Predictive Controllers (N)MPC which often involve optimisation problems with a WS as objective function.