A New Cooperative Distributed MPC Method Based on Reduction and Classification

To decrease the overlarge calculation in-duced by the centralized processing, a new cooperative dis-tributed Model predictive control (MPC) method is pro-posed for large-scale systems with coupled dynamics. Re-duction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups. These groups are mutually decoupled, while there is rel-ativity between these subsystems comprised in the same group. Centralized/cooperative and distributed MPC algo-rithms for each group are implemented to ensure the feasi-bility and the stability of the whole system. Meanwhile, for practical applications, the finite times interactive control strategy between different groups is adopted to compen-sate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC. This algorithm significantly decreases the computational load, has better control performance. Simulations are given to illustrate the effectiveness of these developed algorithms.