TransformerMPPI uses a transformer trained on MPPI-generated trajectories to initialize the mean control sequence, reducing cost and sample counts in navigation and racing simulations.
Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions
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abstract
In this paper, we consider the problem of precise attitude control for geodetic missions, such as the GRACE Follow-on (GRACE-FO) mission. Traditional and well-established control methods, such as Proportional-Integral-Derivative (PID) controllers, have been the standard in attitude control for most space missions, including the GRACE-FO mission. Instead of significantly modifying (or replacing) the original PID controllers that are being used for these missions, we introduce an iterative modification to the PID controller that ensures improved attitude control precision (i.e., reduction in attitude error). The proposed modification leverages Gated Recurrent Units (GRU) to learn and predict external disturbance trends derived from incoming attitude measurements from the GRACE satellites. Our analysis has revealed a distinct trend in the external disturbance time-series data, suggesting the potential utility of GRU's to predict future disturbances acting on the system. The learned GRU model compensates for these disturbances within the standard PID control loop in real time via an additive correction term which is updated at regular time intervals. The simulation results verify the significant reduction in attitude error, verifying the efficacy of our proposed approach.
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Transformer-Based Model Predictive Path Integral Control
TransformerMPPI uses a transformer trained on MPPI-generated trajectories to initialize the mean control sequence, reducing cost and sample counts in navigation and racing simulations.