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The University of Illinois, in collaboration with NASA Jet Propulsion Laboratory (JPL) and NASA Ames Research Center, has developed a novel Attitude Control System (ACS) called the Strain Actuated Solar Arrays (SASA), with sub-milli-arcsecond pointing capability. SASA uses strain-producing actuators to deform flexible deployable structures, and the resulting reaction forces rotate the satellite. This momentum transfer strategy is used for jitter reduction and small-angle slew maneuvers. The system is currently at a Technology Readiness Level of 4-5 and has an upcoming demonstration flight on the CAPSat CubeSat mission. An extension to the SASA concept, known as Multifunctional Structures for Attitude Control (MSAC), enables arbitrarily large-angle slew maneuvers in addition to jitter cancellation. MSAC can potentially replace reaction wheels and control moment gyroscopes for attitude control systems, thereby eliminating a key source of jitter noise. Both SASA and MSAC are more reliable because of fewer failure modes and lower failure rates as compared to conventional ACS, while having an overall smaller mass, volume, and power budget. The paper discusses the advantages of using SASA and MSAC for a wide range of spacecraft and variant mission classes.
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Reinforcement Learning for Spacecraft Attitude Control
Reinforcement learning (RL) has recently shown promise in solving difficult numerical problems and has discovered non-intuitive solutions to existing problems. This study investigates the ability of a general RL agent to find an optimal control strategy for spacecraft attitude control problems. Two main types of Attitude Control Systems (ACS) are presented. First, the general ACS problem with full actuation is considered, but with saturation constraints on the applied torques, representing thruster-based ACSs. Second, an attitude control problem with reaction wheel based ACS is considered, which has more constraints on control authority. The agent is trained using the Proximal Policy Optimization (PPO) RL method to obtain an attitude control policy. To ensure robustness, the inertia of the satellite is unknown to the control agent and is randomized for each simulation. To achieve efficient learning, the agent is trained using curriculum learning. We compare the RL based controller to a QRF (quaternion rate feedback) attitude controller, a well-established state feedback control strategy. We investigate the nominal performance and robustness with respect to uncertainty in system dynamics. Our RL based attitude control agent adapts to any spacecraft mass without needing to re-train. In the range of 0.1 to 100,000 kg, our agent achieves 2% better performance to a QRF controller tuned for the same mass range, and similar performance to the QRF controller tuned specifically for a given mass. The performance of the trained RL agent for the reaction wheel based ACS achieved 10 higher better reward then that of a tuned QRF controller
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- Award ID(s):
- 1653118
- PAR ID:
- 10156483
- Date Published:
- Journal Name:
- 70th International Astronautical Congress
- Page Range / eLocation ID:
- IAC–19–C1.5.2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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