Reinforcement Learning-based Attitude Control for Spacecraft with Reaction Jets:Theory and Experiment
Addressing the challenge of controlling the attitude of jet-propelled spacecraft under constrained thrust amplitude conditions,a novel attitude control algorithm based on a reinforcement learning framework is introduced.The algorithm comprises two neural networks:a control policy and a Lyapunov function.The control policy directly outputs the thrust forces,adhering to thrust amplitude constraints to resolve the thrust allocation and control saturation issues.By introducing a sample-based spacecraft attitude stability theorem,the attitude control algorithm is appropriately designed to ensure that the control policy meets stability constraints,and the proof of stability is provided.Simulation results show that the proposed attitude control algorithm significantly outperforms the mainstream reinforcement learning algorithm and traditional attitude control methods.Directly applied to a semi-physical simulation platform,the control policy effectively accomplishes the large-angle maneuvering task,demonstrating commendable generalization capabilities and robustness.These results substantiate the effectiveness of the proposed reinforcement learning-based attitude control algorithm.
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