Autonomous Satellite Docking via Adaptive Optimal Output Regulation: A Reinforcement Learning Approach (2301.12489v1)

Abstract: This paper describes an online off-policy data-driven reinforcement learning based-algorithm to regulate and control the relative position of a deputy satellite in an autonomous satellite docking problem. The optimal control policy is learned under the framework of output regulation problem and adaptive dynamic programming (ADP) by considering the continuous-time linearized model of the satellite. The linearized model of relative motion is used to describe the motion between satellites, and the satellite docking problem is formulated as a linear optimal output regulation problem, in which the feedback-forward optimal controller is used to track a class of references and rejecting a class of disturbances while maintaining the overall system's closed-loop stability. The optimal control problem is presented using a data-driven reinforcement learning based method to regulate the relative position and velocity of the deputy to safely dock with the chief. Using the adaptive optimal output regulation framework, the learned optimal feedback-feedforward gains guarantee optimal transient and steady state performances without any prior knowledge of the dynamics of the studied system. {The states/input information of the underlying dynamical system are instead used to compute the approximated optimal feedback-feedforward control gain matrices.} Reference tracking and disturbance rejection are achieved in an optimal sense without using any modelling information of the physics of the satellites. {Simulation results are presented and demonstrate the efficacy of the proposed method.