This article investigates the data-based distributed sensor scheduling for a wireless sensor network (WSN), where multiple sensor nodes monitor different linear systems correspondingly. The WSN admits a network topology to formulate a fully distributed sensor scheduling policy, and transmits measured information over a shared wireless channel. Due to the bandwidth limit, at each time only partial sensor nodes can send their measurements to the corresponding remote controller. By introducing a distributed minimum subset extraction mechanism under Q-learning framework, a data-based sensor scheduling algorithm is proposed, which gives an approximate solution to minimizing the [Math Processing Error]H_\infty performance index of the overall closed-loop system, without requiring the knowledge of system parameters. Also, under persistently exciting condition with sufficiently rich enough disturbances, the algorithm converges to the exact optimal solution. The effectiveness of the proposed algorithm is demonstrated with simulation results.

中文翻译：

---

具有 LFT 参数化子系统的 NDS 的结构可识别性


本文研究了无线传感器网络（WSN）中基于数据的分布式传感器调度，其中多个传感器节点相应地监控不同的线性系统。 WSN采用网络拓扑来制定完全分布式的传感器调度策略，并通过共享无线信道传输测量信息。由于带宽限制，每次只有部分传感器节点可以将其测量结果发送到相应的远程控制器。通过引入Q学习框架下的分布式最小子集提取机制，提出了一种基于数据的传感器调度算法，给出了最小化整个闭环系统的[数学处理误差]H_\infty性能指标的近似解，无需了解系统参数。此外，在具有足够丰富的扰动的持续激励条件下，算法收敛到精确的最优解。仿真结果证明了该算法的有效性。