UAV Formation for Cargo Transport by PID Control with Neural Compensation

Sahbi Boubaker (), Carlos Vacca, Claudio Rosales, Souad Kamel, Faisal S. Alsubaei and Francisco Rossomando ()
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Sahbi Boubaker: Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Carlos Vacca: Instituto de Automatica, CONICET San Juan, San Juan CP 5400, Argentina
Claudio Rosales: Instituto de Automatica, CONICET San Juan, San Juan CP 5400, Argentina
Souad Kamel: Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Faisal S. Alsubaei: Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia
Francisco Rossomando: Instituto de Automatica, CONICET San Juan, San Juan CP 5400, Argentina

Mathematics, 2025, vol. 13, issue 16, 1-20

Abstract: Unmanned Aerial Vehicles (UAVs) are known to have limited payloads, which challenges their widespread use in transporting heavy goods. Meanwhile, collaboration between multiple UAVs in performing such a task may be a promising solution. To address the issues associated with the simultaneous use of UAVs, this paper presents a formation control system for transporting a payload suspended via a cable using two UAVs. The control structure is based on a layered scheme that combines a null-space-based kinematic controller with a PID controller associated with each UAV (quadcopters) with a neural correction system. The null-space supervisor controller is designed to generate the desired velocity for the UAV system to maintain formation. This proposal aims to avoid obstacles, balance the weight distribution across each vehicle, and also reduce the payload trajectory tracking error. The PID controller associated with the neural correction system receives these desired speeds and performs dynamic compensation, taking into account parametric uncertainties and dynamic disturbances caused by the movement of the payload coupled to the UAV systems. The stability analysis of the entire control system is performed using Lyapunov theory. Detailed dynamic models of each UAV in the system, the flexible cables, and the payload are presented in a realistic scenario. Finally, numerical simulations demonstrate the good performance of the UAV system control in formation.

Keywords: hierarchical null-space structure; collision avoidance; trajectory tracking; PID control; neural control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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