A Hybrid Offline Optimization Method for Reconfiguration of Multi-UAV Formations

被引:43
作者
Li, Bin [1 ]
Zhang, Jiangwei [2 ]
Dai, Li [3 ]
Teo, Kok Lay [4 ,5 ]
Wang, Song [6 ,7 ,8 ]
机构
[1] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610065, Peoples R China
[2] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China
[3] Beijing Inst Technol, Sch Automat, Beijing 100811, Peoples R China
[4] Sunway Univ, Sch Math Sci, Selangor Darul Ehsan 47500, Malaysia
[5] Tianjin Univ Finance & Econ, Sch Management Sci & Engn, Tianjin 300222, Peoples R China
[6] Curtin Univ, Perth, WA, Australia
[7] Shenzhen Univ, Shenzhen Audencia Business Sch, Shenzhen 518060, Peoples R China
[8] Curtin Univ, Sch Elect Engn Comp & Math Sci, Bentley, WA 6102, Australia
基金
澳大利亚研究理事会;
关键词
Optimal control; Genetic algorithms; Electronic mail; Optimization methods; Mathematical model; Particle swarm optimization; Continuous state inequality constraints; control parameterization; formation reconfiguration; hybrid optimization; simulated annealing; unmanned aerial vehicle (UAV);
D O I
10.1109/TAES.2020.3024427
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Formation reconfiguration of multiple unmanned aerial vehicles (UAVs) is a challenging problem. Mathematically, this problem is an optimal control problem subject to continuous state inequality constraints and terminal state equality constraints. The first challenge is that there are an infinite number of constraints to be satisfied for the continuous state inequality constraints, which makes the problem extremely difficult to be solved. The second challenge is that the control and state are usually both been discretized. This will result in noncontinuous control input. In addition, the discretized system may not always accurately approximate the original system. In this article, a hybrid offline optimization scheme is proposed to tackle these problems. Unlike the existing methods, the state variables are not required to be discretized and continuous control inputs can be obtained. In addition, the continuous state inequality constraints are tackled without increasing the total number of constraints. Simulation results show that the proposed hybrid optimization method outperforms the state-of-the-art method-the hybrid particle swarm optimization and genetic algorithm.
引用
收藏
页码:506 / 520
页数:15
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