@article{YANG2026, 
author = {Xiaohang YANG and Dongxu CHEN and Zhiyuan ZHAO and Jingdong ZHAO and Hong LIU},
title = {A novel dexterity optimization scheme with kinematic uncertainty handling capability for redundant space manipulators},
year = {2026},
journal = {Chinese Journal of Aeronautics},
volume = {39},
number = {1},
keywords = {Dexterity optimization, Model uncertainty, Recurrent Neural Network (RNN), Trajectory tracking, Large space equipment},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103519},
doi = {10.1016/j.cja.2025.103519},
abstract = {In the construction and maintenance for large space equipment, it is essential to ensure the control accuracy and improve the dexterity of the space manipulator. In this paper, a Finite-Time Convergence Kinematic Control (FTCKC) added with Acceleration Level Dexterity Optimization (ALDO) scheme is proposed to solve the kinematic uncertainty and dexterity optimization problems of redundant space manipulators. Concretely, distinguishing from the asymptotic convergence property of traditional adaptive Jacobian methods, the FTCKC scheme is adopted to construct the equality constraint to address the model uncertainty problem, and its error can converge within a finite time. Subsequently, the dexterity index is reconstructed at acceleration level by a multi-level target handling method. Then, the equality constraint, optimization task, and limit constraints are reformulated as a quadratic programming problem. Moreover, a Recurrent Neural Network (RNN) is engineered for the constructed FTCKC-ALDO scheme. Finally, the superiority of the FTCKC-ALDO-RNN scheme is verified by experiments.}
}