TY - JOUR
T1 - Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network
AU - Wang, Xia
AU - Xu, Bin
AU - Cheng, Yixin
AU - Wang, Hai
AU - Sun, Fuchun
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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.
AB - 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.
KW - Disturbance observer
KW - neural networks (NNs)
KW - nonsingular terminal sliding mode (NTSM)
KW - space robot
KW - target capturing
UR - http://www.scopus.com/inward/record.url?scp=85124833923&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2022.3144569
DO - 10.1109/TNNLS.2022.3144569
M3 - 文章
C2 - 35157591
AN - SCOPUS:85124833923
SN - 2162-237X
VL - 34
SP - 7567
EP - 7577
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 10
ER -