arXiv:2606.31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure cases more effectively than traditional attitude control approaches. We explore reinforcement learning (RL) for attitude control in spacecraft re-entry. An industry-standard proportional-integral-derivative controller with gain scheduling serves as a strong baseline for model-free RL and hybrid controllers that combine these two approaches. We formalize the application
The accelerating advancement of deep reinforcement learning, combined with the increasing demand for autonomous and resilient spacecraft, makes this an opportune time for such research.
This development indicates a move towards more adaptive and robust control systems for critical aerospace applications, potentially reducing reliance on traditional methods and improving mission success rates.
The application of DRL to spacecraft attitude control for demanding scenarios like atmospheric re-entry opens the door for systems that can better handle complex, non-linear dynamics and unforeseen failures.
- · Aerospace Industry (Spacecraft Developers)
- · AI/Machine Learning Companies
- · Defence Sectors
- · Space Exploration Agencies
- · Companies reliant solely on traditional control systems
- · Legacy aerospace software providers
More efficient and safer re-entry for spacecraft and potentially other aerial vehicles.
Accelerated development of fully autonomous spacecraft and satellite systems, reducing human intervention.
Enhanced defensive capabilities in space through more agile and resilient satellite platforms.
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Read at arXiv cs.LG