In the literature of transmission scheduling in wireless networked control systems (WNCSs) over shared wireless resources, most research works have focused on partially distributed settings, i.e., where either the controller and actuator, or the sensor and controller are co-located. To overcome this limitation, the present work considers a fully distributed WNCS with distributed plants, sensors, actuators and a controller, sharing a limited number of frequency channels. To overcome communication limitations, the controller schedules the transmissions and generates sequential predictive commands for control. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists at least one stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process problem and develop a deep-reinforcement-learning-based algorithm for solving it. Numerical results show that the proposed algorithm significantly outperforms the benchmark policies.
翻译:在无线网络控制系统(WNCS)对共享无线资源的传输时间安排文献中,大多数研究工作都侧重于部分分布式设置,即控制器和导动器或传感器和控制器合用同一地点。为了克服这一限制,目前的工作认为,在分布式设备、传感器、导动器和控制器中,完全分布式的网络控制系统(WNCS),共享有限的频率频道。为了克服通信限制,控制器安排传输时间并生成顺序预测指令以进行控制。我们利用随机系统理论的元素,取得了WNCS的充分稳定性,即控制器和导动器或传感器和控制器合用同一地点。一旦条件得到满足,至少有一个固定性和确定性时间安排政策可以稳定WNCS的所有工厂。我们通过分析和代表WNCS在可计算有限长度矢量状态方面的每一步成本功能,将最佳传输时间安排问题发展成一个Markov决策程序的问题,并开发一个基于控制和通信系统参数的深度强化学习算法。Numericalal 结果表明,拟议的算法大大超越了它。