论文标题
线性系统的强大分布模型预测控制：分析和合成
Robust distributed model predictive control of linear systems: analysis and synthesis
论文作者
论文摘要
为了提供分布式模型预测控制（DMPC）的鲁棒性，这项工作提出了一种稳健的DMPC公式，用于离散的时间线性系统，受到未知和结合的干扰。利用在具有类别耦合（如车辆排的）应用中看到的某些类别的分布式系统的结构，制定了一种新颖的鲁棒DMPC。所提出的方法的特征是可分离的终端成本和局部稳健的终端集，后者在在线优化问题中进行了自适应估计。基于设定会员方法的约束收紧方法用于确保在存在干扰的情况下对耦合子系统的限制满意度。在此公式下，闭环系统被证明是可行的，并且输入到国家稳定。为了帮助部署拟议的鲁棒DMPC，提出了一种可能的合成方法和设计条件。最后，提供了具有质量弹力抑制系统的模拟结果，以证明提出的强大DMPC。
To provide robustness of distributed model predictive control (DMPC), this work proposes a robust DMPC formulation for discrete-time linear systems subject to unknown-but-bounded disturbances. Taking advantage of the structure of certain classes of distributed systems seen in applications with interagent coupling like vehicle platooning, a novel robust DMPC is formulated. The proposed approach is characterised by separable terminal costs and locally robust terminal sets, with the latter sets adaptively estimated in the online optimisation problem. A constraint tightening approach based on a set-membership approach is used to guarantee constraint satisfaction for coupled subsystems in the presence of disturbances. Under this formulation, the closed-loop system is shown to be recursively feasible and input-to-state stable. To aid in the deployment of the proposed robust DMPC, a possible synthesis method and design conditions for practical implementation are presented. Finally, simulation results with a mass-spring-damper system are provided to demonstrate the proposed robust DMPC.
