This paper presents a novel cloud-edge framework for addressing computational
and energy constraints in complex control systems. Our approach centers around
a learning-based controller using Spiking Neural Networks (SNN) on physical
plants. By integrating a biologically plausible learning method with local
plasticity rules, we harness the efficiency, scalability, and low latency of
SNNs. This design replicates control signals from a cloud-based controller
directly on the plant, reducing the need for constant plant-cloud
communication. The plant updates weights only when errors surpass predefined
thresholds, ensuring efficiency and robustness in various conditions. Applied
to linear workbench systems and satellite rendezvous scenarios, including
obstacle avoidance, our architecture dramatically lowers normalized tracking
error by 96% with increased network size. The event-driven nature of SNNs
minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional
computing requirements). The results demonstrate the system's adjustment to
changing work environments and its efficient use of computational and energy
resources, with a moderate increase in energy consumption of 27.2% and 37% for
static and dynamic obstacles, respectively, compared to non-obstacle scenarios.

提出了一种新的云边框架解决复杂控制系统中的计算和能源限制问题，采用了基于脉冲神经网络 (SNN) 的学习型控制器在物理设备上实施。通过将生物学合理的学习方法与本地可塑性规则结合，我们利用 SNN 的高效性、可扩展性和低延迟，在设备上直接复制云端控制器的控制信号，减少了对常量植物 - 云通信的需求。植物仅在错误超过预定义阈值时更新权重，确保在各种条件下的效率和鲁棒性。在线性工作台系统和卫星交会等场景中（包括避障），我们的架构通过增加网络规模将归一化跟踪误差显著降低了 96%。由于 SNN 的事件驱动特性，其能源消耗最小，仅约为标准计算需求的 111 nJ（0.3%）。结果展示了系统对不断变化的工作环境的调整以及对计算和能源资源的高效利用，与非障碍场景相比，静态和动态障碍物的能源消耗分别增加了 27.2% 和 37%。

基于云边框架的能效事件驱动控制：在线监督学习、脉冲神经网络和本地可塑性规则的集成

A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An  Integration of Online Supervised Learning, Spiking Neural Networks and Local  Plasticity Rules