Abstract
Based on the incremental model, this paper investigates an adaptive dynamic programming (ADP) control scheme for reentry vehicles. First, the attitude dynamics of the reentry vehicle and its incremental form are established, then the ADP-based optimal controller is devised with reference to the dual heuristic dynamic programming (DHP) framework. Expediently, some local incremental parameters that the controller requires can be estimated through recursive least squares (RLS) technology online, replacing the model network that needs to be globally trained in advance in DHP. In addition, a target critic network is introduced to reduce the impact of state changes on the network parameters during the internal iterations of a time step, so it improves the learning stability. Finally, this control strategy is implemented to an attitude control system of the reentry vehicle, and online simulations verify its effectiveness.
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Acknowledgements
This work was supported partially by the National Natural Science Foundation of China under Grant 61873319, 61903146 and 61803162.
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Li, X., Zheng, Z., Liu, L., Cheng, Z., Wang, Y. (2022). Attitude Control of Reentry Vehicle Based on Adaptive Dynamic Programming with Incremental Model. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_268
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DOI: https://doi.org/10.1007/978-981-15-8155-7_268
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