NN-based reinforcement-learning optimal sliding mode control for drag-free and attitude of spacecraft with state constraints

Abstract

This paper investigates a tracking control issue for a class of drag-free spacecraft with state constraints caused by the optical assembly. Firstly, a nonlinear kinematics and dynamics relative equation of coupling system of position and attitude is devised by a 6-degree of freedom (DOF) model of rigid body. Secondly, an optimal control method with a terminal functional is designed to meet state constraints. To solve the Hamilton-Jacobi-Bellman (HJB) equation generated by the optimal control of the nonlinear model, a policy iteration ideal is presented and the final numerical solution of the iteration is obtained by a critic neural network (NN). Thirdly, to enhance the robustness of the closed-loop system, an optimal sliding mode control is proposed, which can ensure the optimal and robustness performance simultaneously. At last, the simulation results demonstrate the performance of the proposed methods and the contrast of precision and robustness of two methods are showcased.