A method for performing an optimized control of a complex dynamical system (sys) using machine learned, scenario based control heuristics, the method comprising the steps of:
providing (S1) a simulation model (f) for predicting a system state vector (x) of said dynamical system (sys) in time based on a current scenario parameter vector (p) and a control vector (u); using (S2) a Model Predictive Control, MPC, algorithm to provide the control vector (u) at every time during a simulation of said dynamical system (sys) using said simulation model (f) for different scenario parameter vectors (p0, p1, p2, ..) and initial system state vectors (x00, x01, x02, ..); calculating (S3) for every simulated combination of a scenario parameter vector (p) and initial system state vector (x 0 ) a resulting optimal control value (u* (p, x 0 )) by the MPC algorithm and saving the resulting optimal control value; generating (S4) machine learned control heuristics (u a(p, x 0 )) approximating the relationship between the corresponding scenario parameter vector (p) and the initial system state vector (x 0 ) for the saved resulting optimal control value (u* (p, x 0 )) using a machine learning algorithm; and using the generated machine learned control heuristics to control (S5) the complex dynamical system (sys) modelled by said simulation model (f).