TY - JOUR
T1 - A Hybrid Offline Optimization Method for Reconfiguration of Multi-UAV Formations
AU - Li, Bin
AU - Zhang, Jiangwei
AU - Dai, Li
AU - Teo, Kok Lay
AU - Wang, Song
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - Continuous state inequality constraints
KW - control parameterization
KW - formation reconfiguration
KW - hybrid optimization
KW - simulated annealing
KW - unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85097683455&partnerID=8YFLogxK
U2 - 10.1109/TAES.2020.3024427
DO - 10.1109/TAES.2020.3024427
M3 - Article
AN - SCOPUS:85097683455
SN - 0018-9251
VL - 57
SP - 506
EP - 520
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 1
M1 - 9199535
ER -