  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.
