Synchronization control scheme for multi-process systems based on model predictive control

Multi-process system (MPS) is an important process system for modern industry. The parallel operating subsystems may have synchronization requirements. A generalized synchronization control scheme is thus developed in this paper based on the model predictive control framework by combining a generalized synchronization cost function and the predictive cost function. The resulted control algorithm indicates that the predictive control errors of each sub-process and the predictive synchronization errors between sub-processes are used together as feedback information in the control scheme to ensure the optimal control performances of each sub-processes as well as synchronization performance, which essentially leads to a multi-input and multi-output (MIMO) control for the MPS. With a proper selection of the synchronization error functions, ratio and distance synchronization controls are conducted with the numerical simulation on an MPS consists of three sub-processes. The results clearly prove the effectiveness, robustness and flexibility of the proposed synchronization control scheme.