Journal article
Robust adaptive learning control of space robot for target capturing using neural network
IEEE Transactions on Neural Networks and Learning Systems, pp.1-11
2022
Abstract
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.
Details
- Title
- Robust adaptive learning control of space robot for target capturing using neural network
- Authors/Creators
- X. Wang (Author/Creator)B. Xu (Author/Creator)Y. Cheng (Author/Creator)H. Wang (Author/Creator)F. Sun (Author/Creator)
- Publication Details
- IEEE Transactions on Neural Networks and Learning Systems, pp.1-11
- Publisher
- IEEE
- Identifiers
- 991005545556207891
- Copyright
- © 2022 IEEE
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.29 Automation & Control Systems
- 4.29.104 Adaptive Control
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic
- ESI research areas
- Computer Science