Neural network–based reconfiguration control for spacecraft formation in obstacle environments |
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| Authors: | Ning Zhou Riqing Chen Yuanqing Xia Jie Huang Guoxing Wen |
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| Affiliation: | 1. College of Computer and Information Sciences, Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Smart Agriculture and Forestry Key Laboratory of Fujian Unverisity, Fujian Agriculture and Forestry University, Fuzhou, China;2. Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands;3. School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, China;4. Department of Mathematics, Binzhou University, Binzhou, China |
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| Abstract: | This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision. |
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| Keywords: | coordination control finite‐time control formation reconfiguration modeling uncertainties neural network obstacle avoidance |
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