This paper is focused on control design for high-precision satellite rendezvous systems.A relative motion model of leader-follower satellites described by relative orbit elements(ROE)is adopted,which has clear geometr...This paper is focused on control design for high-precision satellite rendezvous systems.A relative motion model of leader-follower satellites described by relative orbit elements(ROE)is adopted,which has clear geometric meaning and high accuracy.An improved repetitive control(IRC)scheme is proposed to achieve high-precision position and velocity tracking,which utilizes the advantage of repetitive control to track the signal precisely and conquers the effects of aperiodic disturbances by adding a nonsingular terminal sliding mode(NSTSM)controller.In addition,the nonlinear state error feedback(NLSEF)is used to improve the dynamic performance of repetitive controller and the radial basis function(RBF)neural networks are employed to approximate the unknown nonlinearities.From rigorous Lyapunov analysis,the stability of the whole closed-loop control system is guaranteed.Finally,numerical simulations are carried out to assess the efficiency and demonstrate the advantages of the proposed control scheme.展开更多
基金the National Natural Science Foundation of China(No.61873127)the Key International(Regional)Cooperative Research Projects of the National Natural Science Foundation of China(No.62020106003)。
文摘This paper is focused on control design for high-precision satellite rendezvous systems.A relative motion model of leader-follower satellites described by relative orbit elements(ROE)is adopted,which has clear geometric meaning and high accuracy.An improved repetitive control(IRC)scheme is proposed to achieve high-precision position and velocity tracking,which utilizes the advantage of repetitive control to track the signal precisely and conquers the effects of aperiodic disturbances by adding a nonsingular terminal sliding mode(NSTSM)controller.In addition,the nonlinear state error feedback(NLSEF)is used to improve the dynamic performance of repetitive controller and the radial basis function(RBF)neural networks are employed to approximate the unknown nonlinearities.From rigorous Lyapunov analysis,the stability of the whole closed-loop control system is guaranteed.Finally,numerical simulations are carried out to assess the efficiency and demonstrate the advantages of the proposed control scheme.
