Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation |
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Affiliation: | 1. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada;1. National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi''an 710072, China;2. Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi''an 710072, China;3. North Automatic Control Technology Institute, Taiyuan 030006, China;1. Department of Electrical Engineering, Tamkang University, No. 151, Yingzhuan Road, Tamsui District, New Taipei City 25137, Taiwan;2. Department of Electrical Engineering, Chien Hsin University of Science and Technology, No. 229, Chien Hsin Road, Zhongli City, Taoyuan County 32097, Taiwan;1. Department of Control Science and Engineering, Jilin University, Changchun 130012, China;2. Department of Control Engineering, Changchun University of Technology, Changchun 130012, China |
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Abstract: | This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. |
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Keywords: | Spacecraft control Rendezvous and docking Adaptive neural networks Input saturation Command filter |
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