Data-Based Distributed Sensor Scheduling for Multiple Linear Systems With $H_\infty$ Performance Preservation

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
$H_\infty$
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.