论文标题
使用随机有限集理论对大型协作群的分散控制
Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
论文作者
论文摘要
控制大量的机器人代理提出了许多挑战，包括但不限于由于大量代理而导致的计算复杂性，群体中每个代理的功能的不确定性以及群体配置的不确定性。这项工作的贡献是将随机有限集（RFS）分散，控制大型协作群以控制单个代理。 RFS控制配方假定群体控制的拓扑结构已完成，并以集中式方式使用完整的图。为了以局部或分散的方式概括控制拓扑，稀疏LQR用于稀疏使用迭代LQR获得的RFS控制增益矩阵。这使代理商可以使用彼此（本地化拓扑）或仅代理自己的信息（分散拓扑）的代理信息来做出控制决定。比较了分散的RFS控制的稀疏性和性能在反馈控制收益中的不同程度的定位程度上进行了比较，这表明在为大型协作群提供RFS控制方面，与集中式控制相比，稳定性和性能不会显着降低。
Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize Random Finite Set (RFS) control of large collaborative swarms for control of individual agents. The RFS control formulation assumes that the topology underlying the swarm control is complete and uses the complete graph in a centralized manner. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS control gain matrix obtained using iterative LQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in providing RFS control for large collaborative swarms.
