Work

A novel dexterity optimization scheme with kinematic uncertainty handling capability for redundant space manipulators

Year: 2025

Type: article

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.

Source: Chinese Journal of Aeronautics

DOI: 10.1016/j.cja.2025.103519

Authors: Xiaohang YANG, Dongxu CHEN, Zhiyuan ZHAO, Jingdong ZHAO, Hong LIU

Institutions: Harbin Institute of Technology, State Key Laboratory of Robotics and Systems

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,000

Funder: National Natural Science Foundation of China