提出可调中央被动性控制框架,提升多端口网络系统的稳定性和灵活性。
Tunable Passivity Control for Centralized Multiport Networked Systems
- 设计集中式能量监控与最优耗散分配控制器,实现严格被动性保障。
- 在时变延迟下仍保持L2稳定性,且无需远程节点最小相位假设。
- 适合复杂网络系统如多机器人协同控制,增强系统可扩展性。
集中式多端口网络动态(CMND)系统已成为多领域复杂网络系统的关键架构,如多方遥操作和多智能体控制。该系统由一个中心节点通过网络连接多个远端节点。传统基于被动性的方法虽能稳定小规模系统,但受限于去中心化补偿、灵活性差及对节点被动性的强假设。本文提出一种集中式最优被动性控制框架,包含集中式被动性观测器监测整体能量流,以及最优被动性控制器,在各节点间灵活分配所需耗散,确保严格被动性与L2稳定性。所提数据驱动的免模型方法——可调集中式最优被动性控制(TCoPC),在预设耗散分配策略下优化整体性能,可对部分子网络施加高耗散,同时放松其他节点的耗散要求。仿真结果表明,该框架在多种时变延迟场景下完成复杂任务,且放宽了对远程节点最小相位与被动性的假设,显著提升了系统的可扩展性与泛化能力。
原文摘要 · Abstract (English)
Centralized Multiport Networked Dynamic (CMND) systems have emerged as a key architecture with applications in several complex network systems, such as multilateral telerobotics and multi-agent control. These systems consist of a hub node/subsystem connecting with multiple remote nodes/subsystems via a networked architecture. One challenge for this system is stability, which can be affected by non-ideal network artifacts. Conventional passivity-based approaches can stabilize the system under specialized applications like small-scale networked systems. However, those conventional passive stabilizers have several restrictions, such as distributing compensation across subsystems in a decentralized manner, limiting flexibility, and, at the same time, relying on the restrictive assumptions of node passivity. This paper synthesizes a centralized optimal passivity-based stabilization framework for CMND systems. It consists of a centralized passivity observer monitoring overall energy flow and an optimal passivity controller that distributes the just-needed dissipation among various nodes, guaranteeing strict passivity and, thus, L2 stability. The proposed data-driven model-free approach, i.e., Tunable Centralized Optimal Passivity Control (TCoPC), optimizes total performance based on the prescribed dissipation distribution strategy while ensuring stability. The controller can put high dissipation loads on some sub-networks while relaxing the dissipation on other nodes. Simulation results demonstrate the proposed frameworks performance in a complex task under different time-varying delay scenarios while relaxing the remote nodes minimum phase and passivity assumption, enhancing the scalability and generalizability.
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