An Adaptive Parameter Identification Algorithm for Post-capture of a Tumbling Target

Jia, Xu; Yang, Yang; Peng, Yan; Li, Xiaomao; Zheng, Shuanghua; Cui, Jianxiang

doi:10.1007/978-3-030-27538-9_35

Cited by 2 publications

(1 citation statement)

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“…Identifiability of inertial parameters for 2D serial space robots has been given via matrix set in [16], and the results were acceptable when measurement noise is not obvious. In [28], the variable data-window-size recursive LS algorithm was applied, and the identification equation was similar to the one with unknown initial angular momentum in [17]. In [18], the identification equation for the system with unknown linear and angular momentum were developed, and the modified recursive LS method was presented to estimate the inertial parameters in one-step [19].…”

Section: Introductionmentioning

confidence: 99%

Continuous-discrete extended Kalman filter based parameter identification method for space robots in postcapture

Zhang,

Shi,

Yang

et al. 2023

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Space robots have become increasingly important in on-orbit missions, especially for capturing noncooperative targets. However, a major challenge is that the inertial parameters of these targets are often unknown, but crucial for post-capture tasks. This paper proposes continuous-discrete extended Kalman filter (CD-EKF) based identification methods that rely solely on noisy measurements of the manipulator’s rotation angle, the base’s attitude angle, and its position. The methods exploit the conservation of momentum or the dynamics of space robots to formulate the identification equations and construct the filter. In addition, an analytical solution for computing the Jacobian matrix of the dynamic response of a space robot is derived, and sparse matrix multiplication is used to reduce the computation time. The effectiveness and efficiency of the proposed methods are evaluated through numerical simulations using 2D and 3D models, and Monte Carlo simulations are performed to analyze robustness, noise effects, and initial states. The simulation results confirm the effectiveness of the proposed methods and demonstrate their ability to handle noise and uncertainty.

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