No-reference Path Receding Horizon Control for Multi-UAV Formation Reconfiguration Based on Adaptive Differential Evolution

As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is...

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Bibliographic Details
Published in:International journal of control, automation, and systems Vol. 22; no. 8; pp. 2633 - 2643
Main Authors: Liu, Xin, Chen, Yong, Zhang, Siweihua, Fu, Pengcheng
Format: Journal Article
Language:English
Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.08.2024
Springer Nature B.V
제어·로봇·시스템학회
Subjects:
Adaptive algorithms
Algorithms
Aviation
Control
Efficiency
Energy sources
Engineering
Evolutionary algorithms
Evolutionary computation
Greedy algorithms
Local optimization
Mechatronics
Optimization
Parameterization
Receding horizon principle
Reconfiguration
Regular Papers
Robotics
Unmanned aerial vehicles
제어계측공학
Adaptive differential evolution algorithm
unmanned aerial vehicle
formation reconfiguration
receding horizon control
ISSN:1598-6446, 2005-4092
Online Access:Get full text
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Summary:As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is used to allocate leader and follower positions. Based on the limitations of the receding horizon control (RHC) method and the control parameterization and time discretization (CPTD) method, we propose the no reference path RHC (NRPRHC) method. The proposed method transforms the formation reconfiguration into smaller local optimization problems, leading to a reduction in the size of the optimization stages and computational complexity. For each local optimization problem, we propose the adaptive population differential evolution (APDE) algorithm to optimize the control inputs. Finally, the results are provided to illustrate the feasibility and effectiveness of the proposed method.
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http://link.springer.com/article/10.1007/s12555-023-0600-7
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-023-0600-7