Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network

Xia Wang, Bin Xu, Yixin Cheng, Hai Wang, Fuchun Sun

科研成果: 期刊稿件文章同行评审

33 引用 (Scopus)

摘要

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

源语言英语
页(从-至)7567-7577
页数11
期刊IEEE Transactions on Neural Networks and Learning Systems
34
10
DOI
出版状态已出版 - 1 10月 2023

指纹

探究 'Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network' 的科研主题。它们共同构成独一无二的指纹。

引用此