Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This paper presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. Firstly, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at run-time while significantly reducing computational complexity. Moreover, to overcome the ”short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes, and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of -76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. And the ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Ruijie Fu

Lancong Guo

An Zou

Cailian Chen

Xinping Guan

Yehan Ma

ACM Transactions on Cyber-Physical Systems

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

尽管无线控制是未来工业的关键技术之一，但大多数无线网络仅用于监控。当无线网络用于传输控制命令时，不确定的链路质量和有限的网络资源可能会破坏多回路控制系统的性能。因此，随着网络条件的变化和工厂的演变，合理分配这些资源以优化控制性能至关重要。本文基于自适应动态规划，提出了无线控制系统综合最优调度策略。首先，我们提出了一种有效的自适应动态规划调度（ADPS）策略，该策略在运行时基于单步控制性能解决最优调度问题，同时显著降低计算复杂度。此外，为了克服单步性能预测的“短视”问题，我们将ADPS扩展为ADPS-m（多步预测），通过结合更长时域的工厂演变来优化多步性能。进一步地，我们提出了ADPS-H（异构流调度）以支持具有不同数据速率和尺寸的异构流，并提出了ADPS-H-m（异构流调度的多步预测），其在更长的预测时域内调度异构流。我们在温和假设下证明了所有这些调度策略都能达到最优性和稳定性。通过集成TOSSIM和MATLAB/Simulink进行了大量实验，以评估在四回路和十回路控制系统案例研究中提出的所有方法。仿真结果表明，这些策略在较低计算成本下，在网络和物理干扰下均能有效提升控制性能。在-76 dBm的噪声水平下，对于四回路情况，ADPS实现了与线性规划相同的控制性能，同时节省了99.5%的执行时间。ADPS-m在预测时域为3时，进一步将控制性能提升了高达27.0%，而ADPS-H-m相比轮询和ADPS-H分别提升了高达32.3%和8.4%的性能。十回路案例则表明了所提出方法的有效性和可扩展性。

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	3