TY - GEN
T1 - Data-driven Parameter Estimation for VTOL UAV Using Opposition-Based Pigeon-Inspired Optimization Algorithm
AU - Huo, Mengzhen
AU - Duan, Haibin
AU - He, Hangxuan
AU - Wei, Chen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Inertial physical parameter estimation for a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) is helpful to the control and calibration. A novel bio-inspired method, named opposite-based Pigeon-Inspired Optimization (OBPIO) algorithm, is proposed in this paper based on deterministic and dynamic opposition-based learning (OBL) strategies. The deterministic opposition-based Learning strategy is employed in population initialization and offspring generation to accelerate the convergence speed of the globally optimal pigeon. Meanwhile, the dynamic opposition-based learning strategy is introduced to facilitate the central pigeon of the swarm to explore the potential better region. The proposed algorithm is applied to the VTOL UAV system with the data from model tests. On the contrast, the improved OBPIO algorithm showed better performance in convergence speed and overall search ability.
AB - Inertial physical parameter estimation for a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) is helpful to the control and calibration. A novel bio-inspired method, named opposite-based Pigeon-Inspired Optimization (OBPIO) algorithm, is proposed in this paper based on deterministic and dynamic opposition-based learning (OBL) strategies. The deterministic opposition-based Learning strategy is employed in population initialization and offspring generation to accelerate the convergence speed of the globally optimal pigeon. Meanwhile, the dynamic opposition-based learning strategy is introduced to facilitate the central pigeon of the swarm to explore the potential better region. The proposed algorithm is applied to the VTOL UAV system with the data from model tests. On the contrast, the improved OBPIO algorithm showed better performance in convergence speed and overall search ability.
UR - https://www.scopus.com/pages/publications/85128180482
U2 - 10.1109/ROBIO54168.2021.9739589
DO - 10.1109/ROBIO54168.2021.9739589
M3 - 会议稿件
AN - SCOPUS:85128180482
T3 - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
SP - 669
EP - 674
BT - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
Y2 - 27 December 2021 through 31 December 2021
ER -