摘要：Deep process and machine knowledge is necessary for setting up conventional manufacturing systems. Hence, the degree of automation shall be increased to ensure high productivity and flexibility. An essential element of this goal is the systematic establishment of additional control loops, e. g. in form of machine-oriented control loops or higher process control loops. In the following a method is presented to decouple higher process control loops from machine-oriented control loops. For this a Model-based Predictive Controller (MPC) is used to predict future machine behavior. Based on this prediction the MPC adapts the reference from higher process control loops with respect to machine dynamics and time delay of machine as well as controller computing delays and communication delays. Thereby constraints can be defined to fulfill requirements that are important for the control task or that are given by the higher control loops. This approach is applied to a milling process. First the machine behavior is identified and a machine model with time delay is introduced. Then a Kalman Filter for estimating unknown states and a MPC are designed. An example is presented, where the process force is controlled in order to fulfill a higher objective, e. g. minimum production time. For this case it is shown, that the given method ensures desired process and machine limits with respect to given machine dynamics and time delays. Consequently the presented concept is usable for transferring higher process optimizations to other similar machine types without adaptions in higher process control loops. Only the MPC-based interlayer must be adapted, with respect to machine model and required constraints.
关键词：Model-based controlPredictive ControlClosed-loop identificationMillingManufacturing systemsProduction systemsSelf-optimizing systems
