TY - GEN
T1 - Gaussian Adaptive Mutation Pigeon-Inspired Optimized Backstepping Controller for Aerial Manipulation Trajectory Tracking
AU - Bin, Lin
AU - Wei, Chen
AU - Duan, Haibin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - For aerial manipulation, there are many challenging problems such as coupled force control and compensation of uncertain external disturbance. In this study, the backstepping controller utilizing a full coupled dynamic model is developed to control the Unmanned Aerial Vehicle (UAV) with a manipulator. In addition, the entire system is required to track the desired trajectory when the end-effector of the manipulator is disturbed by a lightweight object. Considering the precision requirement, the control problem is transformed into a controller parameter tuning problem and as well as makes full use of the heuristic optimization algorithm to search for a set of suitable parameters. Furthermore, in view of the difficulty of parameter tuning of the backstepping controller, a Gaussian Adaptive Mutation Pigeon-Inspired Optimization (GAMPIO) algorithm is proposed. Finally, a series of simulations, compared with other optimization algorithms, are conducted to demonstrate the GAMPIO’s superiority. The results verify that GAMPIO has faster convergence and better exploration capability to make end-effector performances better in trajectory tracking.
AB - For aerial manipulation, there are many challenging problems such as coupled force control and compensation of uncertain external disturbance. In this study, the backstepping controller utilizing a full coupled dynamic model is developed to control the Unmanned Aerial Vehicle (UAV) with a manipulator. In addition, the entire system is required to track the desired trajectory when the end-effector of the manipulator is disturbed by a lightweight object. Considering the precision requirement, the control problem is transformed into a controller parameter tuning problem and as well as makes full use of the heuristic optimization algorithm to search for a set of suitable parameters. Furthermore, in view of the difficulty of parameter tuning of the backstepping controller, a Gaussian Adaptive Mutation Pigeon-Inspired Optimization (GAMPIO) algorithm is proposed. Finally, a series of simulations, compared with other optimization algorithms, are conducted to demonstrate the GAMPIO’s superiority. The results verify that GAMPIO has faster convergence and better exploration capability to make end-effector performances better in trajectory tracking.
KW - Aerial manipulation
KW - Backstepping controller tuning
KW - Gaussian distribution
KW - Pigeon-Inspired Optimization (PIO)
KW - Stagnation mutation
UR - https://www.scopus.com/pages/publications/85151159215
U2 - 10.1007/978-981-19-6613-2_272
DO - 10.1007/978-981-19-6613-2_272
M3 - 会议稿件
AN - SCOPUS:85151159215
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 2800
EP - 2809
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -