Predefined-time repetitive learning control of robotic manipulators
A backstepping-based predefined-time repetitive learning control scheme is proposed for uncertain robot manipulators to achieve rapid and high-precision tracking control performance.A non-singular predefined-time virtual controller is constructed to effectively avoid the singularity issues caused by the differentiation of the virtual controller in conventional finite-time backstepping design.It ensures that the tracking error of the robot manipulators joint positions converges to a neighborhood of the origin within the predefined time.Then,the lumped uncertainty of the manipulator is separated into periodic and non-periodic parts by considering the periodic characteristics of the desired trajectory.A fully saturated repetitive learning law is constructed to accurately estimate and compensate for the periodic uncertainty.Meanwhile,a robust control law is developed and the terminal attracting technique is applied to guarantee the effective compensation of the non-periodic uncertainty including external disturbances,such that the high-precision tracking of the robot manipulators joint positions is achieved.Finally,the stability of the closed-loop system and the error convergence performance of the proposed scheme are analyzed through the Lyapunov stability synthesis.The effectiveness of the proposed control method is verified by comparative simulations.
robot manipulatorpredefined-time controlrepetitive learning lawbackstepping recursive algorithmperiodic uncertaintytracking control
万方数据
维普
