TY - GEN
T1 - Hybrid ISMC-PIO and Receding Horizon Control for UAVs Formation
AU - Xu, Xiaobin
AU - Duan, Haibin
AU - Deng, Yimin
AU - Luo, Delin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Unmanned aerial vehicles (UAVs) formation can achieve considerable missions. Control strategy plays an important role in UAVs formation. In this paper, a receding horizon control (RHC) for UAVs formation based on independent search and multi-area convergence pigeon-inspired optimization (ISMC-PIO) is proposed. To minimize the cost value for measuring UAVs formation process, the modified pigeon-inspired optimization (PIO) is utilized by converting the RHC parameters and performance index for UAVs formation problem to a global optimization problem. PIO is a novel bioinspired algorithm. However, basic PIO has the disadvantages of slower convergence speed and falling into local optimum easily. The modified PIO has faster convergence rate and global search ability by importing independent search factor and multi-area convergence strategy. Numerous experiments are implemented to prove that the ISMC-PIO can converge quickly and obtain a better cost value.
AB - Unmanned aerial vehicles (UAVs) formation can achieve considerable missions. Control strategy plays an important role in UAVs formation. In this paper, a receding horizon control (RHC) for UAVs formation based on independent search and multi-area convergence pigeon-inspired optimization (ISMC-PIO) is proposed. To minimize the cost value for measuring UAVs formation process, the modified pigeon-inspired optimization (PIO) is utilized by converting the RHC parameters and performance index for UAVs formation problem to a global optimization problem. PIO is a novel bioinspired algorithm. However, basic PIO has the disadvantages of slower convergence speed and falling into local optimum easily. The modified PIO has faster convergence rate and global search ability by importing independent search factor and multi-area convergence strategy. Numerous experiments are implemented to prove that the ISMC-PIO can converge quickly and obtain a better cost value.
KW - independent search and multi-area convergence (IS MC)
KW - pigeon-inspired optimization (PIO)
KW - receding horizon control (RHC)
KW - unmanned aerial vehicle (UAV)
UR - https://www.scopus.com/pages/publications/85071301753
U2 - 10.1109/CEC.2019.8789890
DO - 10.1109/CEC.2019.8789890
M3 - 会议稿件
AN - SCOPUS:85071301753
T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
SP - 3277
EP - 3284
BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019
Y2 - 10 June 2019 through 13 June 2019
ER -