To assist redundant manipulator to complete complex repetitive trajectory in a presented time, this article provides a solution to the kinematic assignment and presents a predefined time fuzzy zeroing neural network with event-triggered mechanism (ETM-PTFZNN). The repetitive kinematics of the redundant manipulator is originally formulated as a time-varying quadratic programming (TVQP) problem, and the ETM-PTFZNN is engaged to solve the corresponding TVQP, where the fuzzy predefined time (PT) convergence is obtained by the fuzzy system and PT activation function simultaneously. Furthermore, event-triggered mechanism is introduced to update the fuzzy parameters of the ETM-PTFZNN orderly, which greatly alleviates the calculation burden of the ETM-PTFZNN. Theoretical analyses and simulative profiles reveal that the ETM-PTFZNN model can realize PT characteristic, robustness, adaptive stability, and repetitive trajectory for the TVQP problem of kinematic planning for manipulators.