Method for controlling an automation process in real time on the basis of at least one change profile of at least one process variable, wherein a change profile describes a course of the process variable with respect to an argument of the process variable, the method steps comprising: determining a first change profile 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 of the process variable, 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 by means of a numerical algorithm on the basis of the first change profile, comprising: adapting a selected profile function to the first change profile by means of a numerical adaptation process, wherein, during the adaptation of the profile function to the first change profile, consideration is given to the profile function fulfilling the boundary condition; and identifying the adapted profile function as a second change profile; checking whether the second change profile satisfies at least one secondary condition of the process variable; controlling the automation process on the basis of the second change profile if the second change profile satisfies the at least one secondary condition; and controlling the automation process on the basis of a predetermined fallback profile if the second change profile does not satisfy the at least one secondary condition. determining a first change profile 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 of the process variable, 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 by means of a numerical algorithm on the basis of the first change profile, comprising: adapting a selected profile function to the first change profile by means of a numerical adaptation process, wherein, during the adaptation of the profile function to the first change profile, consideration is given to the profile function fulfilling the boundary condition; and identifying the adapted profile function as a second change profile; adapting a selected profile function to the first change profile by means of a numerical adaptation process, wherein, during the adaptation of the profile function to the first change profile, consideration is given to the profile function fulfilling the boundary condition; and identifying the adapted profile function as a second change profile; checking whether the second change profile satisfies at least one secondary condition of the process variable; controlling the automation process on the basis of the second change profile if the second change profile satisfies the at least one secondary condition; and controlling the automation process on the basis of a predetermined fallback profile if the second change profile does not satisfy the at least one secondary condition.