IEEE Trans Cybern. 2019 Dec;49(12):4117-4128. doi: 10.1109/TCYB.2018.2859159. Epub 2018 Sep 10.
In this paper, we investigate the output containment control problem for a network of heterogeneous linear multiagent systems. The control target is to drive the outputs of the followers into the convex hull spanned by the leaders. To this end, we first derive a necessary condition imposed on both system dynamics and network topology from the viewpoint of internal model principle. Then, based on the necessary condition, we utilize a dynamic controller to drive the outputs of the leaders and followers to track the reference trajectories to achieve containment exponentially. We consider a general network topology which only contains a united spanning tree. Both fixed and dynamic network topologies are taken into consideration. Then, the optimal control problem for containment is further studied. An optimal control law is constructed from an algebraic Riccati equation, which is proved to be a stabilizing one as well. Finally, a reinforcement learning algorithm is introduced to solve the optimal control problem on line without the knowledge the system dynamics. Simulations are given at last to validate our theoretical findings.
本文研究了一类异构线性多智能体系统的输出包含控制问题。控制目标是将跟随者的输出驱动到领导者的凸包内。为此，我们首先从内模原理的角度推导出了系统动态和网络拓扑结构所必需的条件。然后，基于必要条件，我们利用动态控制器驱动领导者和跟随者的输出跟踪参考轨迹，实现指数级的包含。我们考虑了一种仅包含一个统一生成树的一般网络拓扑结构。同时考虑了固定和动态网络拓扑结构。然后，进一步研究了包含的最优控制问题。从代数 Riccati 方程构造了最优控制律，该方程被证明也是稳定的。最后，引入了一种强化学习算法在线求解最优控制问题，而无需了解系统动态。最后给出了仿真结果来验证我们的理论发现。
