Cooperative distributed model predictive control with N-step accessible information

The Distributed Model Predictive Control (DMPC) has been more and more popular in the control of large-scale systems due to features of error tolerance, reliability and structural flexibility. The information topology is important and should be concerned in distributed control. Consider a large system with subsystems interacting by both states and inputs, this paper proposed an N-step accessible matrix based information selection method and corresponding cooperative DMPC design method is proposed, where the optimization range of each subsystem is determined by the union of the all the N-step associated matrix over the predictive horizon. To further simplified the topology, impact subsystems are replaced in incremental formats with increments caused by changes of current controller. In this way, local subsystem only communicate with N-step neighbors. The resulting DMPC is able to achieve “Pareto Optimality” with reduced communication burdens. The simulation results show the effectiveness of the proposed method.