论文标题
可重新配置的插件分布式模型预测控制用于参考跟踪
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking
论文作者
论文摘要
提出了插件模型预测控制（PNP MPC）方案，用于不同的流程网络，以跟踪分段常数参考。提出的方案允许子系统偶尔加入并离开网络，同时保持渐近稳定性和递归可行性，并包括两个主要阶段。在重新设计阶段，基于被动性的控制用于确保保留网络的渐近稳定性。在过渡阶段，使用可重构终端成分来确保PNP操作后最初可行的分布式MPC问题。通过将其应用于大规模弹簧抑制系统的网络并将其与基准方案进行比较来评估所提出方案的功效。发现新颖的重新设计阶段会导致PNP操作更快，而新颖的过渡阶段通过接受更多请求来提高灵活性。
A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to occasionally join and leave the network while preserving asymptotic stability and recursive feasibility and comprises two main phases. In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved. In the transition phase, reconfigurable terminal ingredients are used to ensure that the distributed MPC problem is initially feasible after the PnP operation. The efficacy of the proposed scheme is evaluated by applying it to a network of mass-spring-damper systems and comparing it to a benchmark scheme. It is found that the novel redesign phase results in faster PnP operations, whereas the novel transition phase increases flexibility by accepting more requests.
