IEEE Trans Cybern. 2019 Jan;49(1):122-132. doi: 10.1109/TCYB.2017.2766762. Epub 2017 Nov 9.
This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.
本文针对多智能体系统的分布式优化问题，利用基于边的固定时间一致性方法进行研究。在时不变代价函数的情况下，提出了一种新的分布式协议，在最小化已知个体代理的局部凸函数和的同时，实现固定时间的状态一致。在时变代价函数的情况下，基于时不变代价函数情况下的新分布式协议，考虑 Hessian 矩阵，提供了一种分布式协议。在这两种情况下，都推导出了稳定性条件，以确保在固定和切换通信拓扑下解决分布式优化问题。本文结果的一个显著特点是，可以在不依赖代理初始状态的情况下估计一致性的 settling 时间上限，并且可以通过调整系统参数将其任意减小。因此，本文的结果可适用于军事目的的无人机会合等未知环境，同时优化局部目标。提供了电池组功率输出协议的案例研究，以验证理论结果的有效性。
