Distributed Stochastic MPC for Networked Linear Systems With a Multirate Sampling Mechanism
针对具有多个动态子系统的网络化线性系统，提出一种带多速率采样机制的分布式随机模型预测控制算法，处理不同采样周期、随机扰动和概率约束，并补偿数据包丢失，保证系统递归可行性和二次稳定性。
In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.