Air-ground coordinated unmanned swarm systems: A multitasking framework for control design

Xiuye Wang; Huiming Wang; Qinqin Sun; Ye-Hwa Chen

doi:10.1016/j.isatra.2023.12.003

Air-ground coordinated unmanned swarm systems: A multitasking framework for control design

ISA Trans. 2024 Feb:145:315-329. doi: 10.1016/j.isatra.2023.12.003. Epub 2023 Dec 2.

Authors

Xiuye Wang¹, Huiming Wang², Qinqin Sun³, Ye-Hwa Chen⁴

Affiliations

¹ School of Mechanical Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China. Electronic address: xiuyewang@njust.edu.cn.
² School of Mechanical Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China. Electronic address: milletwang1999@163.com.
³ College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China. Electronic address: sun421529@126.com.
⁴ George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, GA 30332, Atlanta, United States of America. Electronic address: yehwa.chen@me.gatech.edu.

PMID: 38097469
DOI: 10.1016/j.isatra.2023.12.003

Abstract

An air-ground heterogeneous unmanned swarm system coordination is considered. The system consists of N unmanned aerial vehicles (UAVs) and one unmanned ground vehicle (UGV). This forms a complicated mission, which consists of the following four different tasks. First, the aerial vehicles are in a compact formation, while avoiding collision with each other. Second, the aerial vehicles should stay close to the ground, while avoiding collision with the ground. Third, the aerial vehicles should stay close to the ground vehicle. Fourth, the ground vehicle should follow a desired trajectory. These tasks reflect two seemingly contradictory nature: close to (due to tracking) and away from (due to avoidance). The effective control design should address all four tasks even in the presence of uncertainty. By two creative transformations, this multitude of tasks are consolidated in a χ-measure. An adaptive robust control, which includes a robust control scheme and an online adaptation law, is then proposed to render guarantee boundedness performance of this χ-measure. As a result, the control design is able to accomplish the combined tracking-avoidance mission for the uncertain swarm system. Despite the presence of conflicting aspects between these tasks, the designed controller exhibits outstanding performance.

Keywords: Adaptive robust control; Air–ground coordination; Tracking-avoidance; Uncertainty; Unmanned swarm system.