Version 1
: Received: 30 October 2024 / Approved: 30 October 2024 / Online: 31 October 2024 (09:18:48 CET)
How to cite:
Hua, Z.; Fu, Z.; Niu, L. Thrust and Pressure Control in Solid Propulsion System via Reinforcement Learning. Preprints2024, 2024102461. https://doi.org/10.20944/preprints202410.2461.v1
Hua, Z.; Fu, Z.; Niu, L. Thrust and Pressure Control in Solid Propulsion System via Reinforcement Learning. Preprints 2024, 2024102461. https://doi.org/10.20944/preprints202410.2461.v1
Hua, Z.; Fu, Z.; Niu, L. Thrust and Pressure Control in Solid Propulsion System via Reinforcement Learning. Preprints2024, 2024102461. https://doi.org/10.20944/preprints202410.2461.v1
APA Style
Hua, Z., Fu, Z., & Niu, L. (2024). Thrust and Pressure Control in Solid Propulsion System via Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202410.2461.v1
Chicago/Turabian Style
Hua, Z., Zhuang Fu and Lu Niu. 2024 "Thrust and Pressure Control in Solid Propulsion System via Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202410.2461.v1
Abstract
A reinforcement learning control method for a solid attitude and divert propulsion system is proposed. The system in this research includes 4 divert thrust nozzles, 6 attitude thrust nozzles, and a common combustion chamber. To achieve the required thrust, the pressure in the combustion chamber is first adjusted by controlling the total opening of the nozzles to generate the gas source. Next, by controlling the opening of nozzles at different positions, the required thrust is produced in five-axis directions. Finally, the motor speed is regulated to drive the valve core to the specified position, completing the closed-loop control of the nozzle opening. The control algorithm used is the Proximal Policy Optimization (PPO) reinforcement learning algorithm. Through system identification and numerical modeling, the training environment for the intelligent agent is created. To accommodate different training objectives, multiple reward functions are implemented. Ultimately, through training, a multi-layer intelligent agent architecture for pressure, thrust, and nozzle opening is established, achieving effective system pressure and thrust control.
Keywords
Solid propulsion system; Attitude and divert nozzles; Thrust and pressure control; Reinforcement learning
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
