Method and system for providing an optimized control of a complex dynamical system

A control heuristic generation platform for providing machine learned control heuristics used for controlling a dynamical system modelled by a simulation model stored in a model storage) and adapted to predict a system state vector of said dynamical system in time based on a current scenario parameter vector and a control vector, wherein said control heuristic generation system comprises:

• a first computation unit using a model predictive control, MPC, algorithm to provide the control vector at every time during a simulation of said dynamical system using said simulation model for different scenario parameter vectors and initial system state vectors and adapted to calculate for every simulated combination of scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm and saving the resulting optimal control value to a memory;
• a second computational unit adapted to
• generate machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the saved resulting optimal control value using a machine learning algorithm, wherein the generated machine learned control heuristics are transferable to a controller of said dynamical system via an interface of said control heuristic generation platform.