Abstract
The traditional methods of attitude control of satellite are represented by PID control, adaptive control, optimal control and intelligent control, etc. With these methods, lots of work of parameter adjustment and simulation needs to do on the earth. We proposed a method based on Deep Deterministic Policy Gradient (DDPG) to learn attitude control strategy in orbit in order to reduce the work and establish the ability of adapting to space environment. Through constructing training environment by using the attitude control system of satellite platform (ACSoSP), we trained an attitude control model and used the model to generate the strategy of attitude control. Validate the method by experiments in simulation environment.
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References
Lillicrap, T.P.: Continuous control with deep reinforcement learning. In: ICLR (2016)
Lu, J.: Study on attitude control algorithm of three-axis stabilization satellite. Ph.D. thesis, Harbin Institute of Technology (2007)
RUMMERY: On-Line Q-Learning Using Connectionist Systems. University of Cambridge, Cambridge (1994)
Silver, D.: Deterministic policy gradient algorithms. In: ICML (2014)
Sutton, R.S.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)
Watkins, C.J.: Learning from delayed rewards. Ph.D. thesis. University of Cambridge, Cambridge (1989)
Williams, R.J.: Simple statistical gradient-following algorithms forconnectionist reinforcement learning. Mach. Learn. 8(2), 229–256 (1992)
Yu, J.: Modern Micro-Sat Technology and Application. Shanghai Popular Technology Press, Shanghai (2004)
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Zhang, J., Wu, F., Zhao, J., Xu, F. (2019). A Method of Attitude Control Based on Deep Deterministic Policy Gradient. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_18
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DOI: https://doi.org/10.1007/978-981-13-7986-4_18
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