论文标题
一个可靠的分布式模型预测控制框架，用于具有输入约束和不同延迟的多代理系统共识
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
论文作者
论文摘要
本文研究了一般线性离散时间多代理系统（MAS）的共识问题，该系统具有输入限制和有限的随时间变化的通信延迟。我们提出了一个可靠的分布式模型预测控制（DMPC）共识协议，该协议将离线共识设计与在线DMPC优化集成在一起，以利用其各自的优势。更确切地说，每个代理都配备了离线共识协议，该协议是先验设计的，具体取决于其直接邻居的估计状态。此外，在轻度的技术假设下，由于不精确的相邻信息而导致的估计误差随着时间的流逝而传播，这是根据该假设的限制的，基于该估计，故意设计了强大的DMPC策略，以实现强大的共识，同时满足输入约束。此外，可以证明，通过适当设计的成本功能和约束，可以递归确保相关优化问题的可行性。我们进一步提供了在有界变化延迟的情况下受约束MAS的共识收敛结果。最后，给出了两个数值示例，以验证所提出的分布共有算法的有效性。
This paper studies the consensus problem of general linear discrete-time multi-agent systems (MAS) with input constraints and bounded time-varying communication delays. We propose a robust distributed model predictive control (DMPC) consensus protocol that integrates the offline consensus design with online DMPC optimization to exploit their respective advantages. More precisely, each agent is equipped with an offline consensus protocol, which is a priori designed, depending on its immediate neighbors' estimated states. Further, the estimation errors propagated over time due to inexact neighboring information are proved bounded under mild technical assumptions, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying input constraints. Moreover, it is shown that, with the suitably designed cost function and constraints, the feasibility of the associated optimization problem can be recursively ensured. We further provide the consensus convergence result of the constrained MAS in the presence of bounded varying delays. Finally, two numerical examples are given to verify the effectiveness of the proposed distributed consensus algorithm.
