Distributed matrix-weighted fusion model predictive control algorithm

This paper is concerned with model predictive control (MPC) for multisensor linear systems. In order to fully utilize information and achieve higher control accuracy, two fusion MPC algorithms are proposed. The first algorithm is named estimation-fusion model predictive controller (E-F MPC), which local estimations are fused and then fusion control input is obtained by the fusion estimation. The second algorithm is named control-fusion model predictive controller (C-F MPC), which fuses the control inputs obtained from local systems. The relationships, differences and complexity of the two algorithms are analyzed. The advantages of E-F MPC are that it is simple and easy to implement with high control accuracy, while the disadvantage is the bad robustness because of the heavy estimation, prediction and control tasks for the fusion node. For C-F MPC, with a small loss of accuracy, network throughput and computational cost of its fusion node will be greatly reduced, which makes it good robustness and flexibility. Simulation analysis verifies the effectiveness and correctness of the conclusion.