Application of TD based reinforcement learning in satellite attitude control

With higher requirements on the accuracy, robustness and disturbance rejection ability in satellite attitude control system, a fuzzy neural control approach applied to the three-axis stabilized satellite is presented. In order to solve problems of online learning and tuning of fuzzy neural network parameters, reinforcement learning based on temporal difference (TD) is proposed and studied, so that training samples for the self-learning controllers are no longer needed. Simulation results showed that the proposed control method with reinforcement learning architecture could not only improve the accuracy and robustness of the system, but could also deal with the uncertainties and external disturbance efficiently.