Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

2026-02-01

AI Summary

This work addresses the challenge in low-altitude economies where unmanned aerial vehicles (UAVs) serving as aerial base stations face a fundamental trade-off between stringent onboard resource constraints and the critical need for control stability, rendering traditional throughput-oriented designs inadequate for ensuring reliability across heterogeneous tasks. To resolve this, the authors propose an integrated perception–communication–computation–control closed-loop framework that, for the first time, incorporates Lyapunov stability theory into resource allocation. The approach explicitly models the impact of communication latency on physical control dynamics and translates it into quantifiable resource constraints. Building upon this, a Stackelberg game is formulated to enable dynamic resource pricing and request optimization, complemented by a lightweight pruned proximal policy optimization (PPO) algorithm to reduce edge deep reinforcement learning overhead. Simulations demonstrate that the proposed scheme effectively guarantees control stability while enhancing overall system utility in dynamic low-altitude environments.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming. However, the efficacy of such heterogeneous networks is often compromised by the conflict between limited onboard resources and stringent stability requirements. Moving beyond traditional throughput-centric designs, we propose a Sensing-Communication-Computing-Control closed-loop framework that explicitly models the impact of communication latency on physical control stability. To guarantee mission reliability, we leverage the Lyapunov stability theory to derive an intrinsic mapping between the state evolution of the control system and communication constraints, transforming abstract stability requirements into quantifiable resource boundaries. Then, we formulate the resource allocation problem as a Stackelberg game, where UAVs (as leaders) dynamically price resources to balance load and ensure stability, while users (as followers) optimize requests based on service urgency. Furthermore, addressing the prohibitive computational overhead of standard Deep Reinforcement Learning (DRL) on energy-constrained edge platforms, we propose a novel and lightweight pruning-based Proximal Policy Optimization (PPO) algorithm. By integrating a dynamic structured pruning mechanism, the proposed algorithm significantly compresses the neural network scale during training, enabling the UAV to rapidly approximate the game equilibrium with minimal inference latency. Simulation results demonstrate that the proposed scheme effectively secures control loop stability while maximizing system utility in dynamic low-altitude environments.

Problem

• Lyapunov stability
• resource allocation
• low-altitude economy
• UAV networks
• control stability

Innovation

• Lyapunov stability
• Stackelberg game
• pruning-based DRL
• control-oriented resource allocation
• low-altitude economy