Abstract: A periodic adaptive tracking compensating learning algorithm is proposed for a class of Brunovsky standard nonlinear uncertain systems with time delay and input saturation. By restructuring the system according to signal replacement theory and functional transformation with the lowest common cycle, the delay and other time-varying parameters are combined into an auxiliary time-varying parameter. Then a periodic adaptive learning algorithm is designed to estimate the auxiliary parameter for approximating and compensating the section which exceeds the saturated limit by a compensator. Finally a comprehensive controller is constituted so that the system state can track the bounded expected value and the repeated iterative learning control problem based on periodic system with input saturation is solved. It is proved that the track error is convergent and all the closed-loop signals are bounded by the difference calculation of Lyapunov-Krasovskii composite energy function. The torque control simulation of common coupled nonlinear manipulator further confirms the effectiveness of the algorithm.