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Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

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Abstract

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

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Authors and Affiliations

  1. Department of Equipment Management and UAV Engineering, Air Force Engineering University, Baling Road No.1, Xiwang Street, Baqiao District, Xi’an, 710038, Shanxi province, China

    Boyang Zhang, Xiuxia Sun & Shuguang Liu

  2. Department of Electrical Engineering College, Anhui Polytechnic University, Bejing Mid-Road, Jiujiang District, Wuhu, 241000, Anhui province, China

    Xiongfeng Deng

Authors
  1. Boyang Zhang
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  2. Xiuxia Sun
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  3. Shuguang Liu
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  4. Xiongfeng Deng
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Correspondence to Boyang Zhang.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Dongjun Lee under the direction of Editor Chan Gook Park. This work is supported by the Aeronautical Science Foundation of China under Grant No. 20155896025.

Boyang Zhang is currently pursuing a Ph.D degree in Equipment Management and UAV Engineering College, Air Force Engineering University, China. His research interests include the multi-UAVs coordination control, predictive control, optimization algorithm.

Xiuxia Sun received her Ph.D. degree in Control Science and Engineering from Beihang University, China, in 1999. She is currently working as a Professor in the Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi’an, China. Her research interests include robust control, adaptive control, flight control.

Shuguang Liu received his Ph.D. degree in Control Science and Engineering from Air Force Engineering University, China, in 2011. He is currently working as an Associate Professor in the Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi’an, China. His research interests include adaptive control, formation flight control.

Xiongfeng Deng received his Ph.D. degree in Control Science and Engineering from Air Force Engineering University, China, in 2019. He is currently working as a Lecturer in the Electrical Engineering College, Anhui Polytechnic University, Wuhu, China. His research interests include cooperative control of multi-agent systems, iterative learning control.

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Zhang, B., Sun, X., Liu, S. et al. Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration. Int. J. Control Autom. Syst. 17, 3009–3020 (2019). https://doi.org/10.1007/s12555-018-0421-2

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  • DOI: https://doi.org/10.1007/s12555-018-0421-2

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