Sensor-Enabled Nested Networked Control for Speed Synchronization and Swing Damping in Air-Ground Collaborative Distribution.
Jingwen Huang, Haina Wang
Abstract
Open AccessWith the rapid development of the low-altitude economy, UAV logistics delivery systems have garnered widespread attention due to their flexibility and efficiency. The cooperative delivery mode involving a UAV with a suspended payload and a ground vehicle represents a typical networked distribution scenario, whose performance is constrained by the tight coupling of sensing, communication, and control. In practical applications, sensor measurement noise and sudden disturbances propagate through the closed-loop system, severely degrading velocity synchronization and swing angle stability. To address this challenge, this paper focuses on a quadrotor UAV slung-load system and proposes a three-layer nested networked closed-loop control architecture for simultaneous velocity tracking of a moving ground target and swing angle stabilization. First, by establishing the system's dynamic model, the mapping relationship between cable tension and the payload swing angle (based on sensor feedback) is revealed. Then, by setting the payload velocity as the outermost control objective and constructing a coupled error to drive a virtual swing angle actuator, the direct impact of noise in the raw sensor data is effectively mitigated. Subsequently, the desired acceleration of the UAV is derived through inverse computation, achieving synchronous optimization of velocity tracking and swing angle suppression. Theoretical analysis using Lyapunov methods demonstrates the stability of the closed-loop system in the presence of bounded delays. Simulation results show that the proposed method effectively suppresses payload swing, controls velocity synchronization error, and exhibits strong robustness against sensor noise and sudden disturbance. This study provides a control solution that improves the precision and robustness of sensor-enabled networked control systems in complex dynamic scenarios.