Method for controlling an automation process in real time on the basis of at least one change profile (P) of at least one process variable (PV), wherein a change profile (P) describes a course of the process variable (PV) with respect to an argument (X) of the process variable (PV), the method steps comprising: determining a first change profile (P1) by means of a recognition process that has real-time capability and is based on a non-linear optimization process while taking into account at least one boundary condition (BC) of the process variable (PV), wherein the recognition process that has real-time capability and is based on a non-linear optimization process is implemented by an artificial neural network, and wherein a non-linear optimization process is carried out in a learning process of the artificial neural network; determining a second change profile (P2) by means of a numerical algorithm on the basis of the first change profile (P1), comprising: adapting a selected profile function (PF) to the first change profile (P1) by means of a numerical adaptation process, wherein, during the adaptation of the profile function (PF) to the first change profile (P1), consideration is given to the profile function (PF) fulfilling the boundary condition (BC); and identifying the adapted profile function (PF) as a second change profile (P2); checking whether the second change profile (P2) satisfies at least one secondary condition (SC) of the process variable (PV); controlling the automation process on the basis of the second change profile (P2) if the second change profile (P2) satisfies the at least one secondary condition (SC); and controlling the automation process on the basis of a predetermined fallback profile (P3) if the second change profile (P2) does not satisfy the at least one secondary condition (SC). determining a first change profile (P1) by means of a recognition process that has real-time capability and is based on a non-linear optimization process while taking into account at least one boundary condition (BC) of the process variable (PV), wherein the recognition process that has real-time capability and is based on a non-linear optimization process is implemented by an artificial neural network, and wherein a non-linear optimization process is carried out in a learning process of the artificial neural network; determining a second change profile (P2) by means of a numerical algorithm on the basis of the first change profile (P1), comprising: adapting a selected profile function (PF) to the first change profile (P1) by means of a numerical adaptation process, wherein, during the adaptation of the profile function (PF) to the first change profile (P1), consideration is given to the profile function (PF) fulfilling the boundary condition (BC); and identifying the adapted profile function (PF) as a second change profile (P2); adapting a selected profile function (PF) to the first change profile (P1) by means of a numerical adaptation process, wherein, during the adaptation of the profile function (PF) to the first change profile (P1), consideration is given to the profile function (PF) fulfilling the boundary condition (BC); and identifying the adapted profile function (PF) as a second change profile (P2); checking whether the second change profile (P2) satisfies at least one secondary condition (SC) of the process variable (PV); controlling the automation process on the basis of the second change profile (P2) if the second change profile (P2) satisfies the at least one secondary condition (SC); and controlling the automation process on the basis of a predetermined fallback profile (P3) if the second change profile (P2) does not satisfy the at least one secondary condition (SC). <EOS>