In order to make a robot precisely track desired periodic trajectories, a terminal sliding mode based repetitive learning control method is proposed, which incorporates characteristics of terminal sliding mode control into repetitive learning control. The hybrid control schemes utilize learning-based feedforward terms to compensate for periodic dynamics and terminal sliding mode-based feedback terms to compensate for nonperiodic dynamics. Advantages of the proposed control include the absence of model parameter in the control law formulation and improved robustness and tracking performance compared with the conventional approaches. The Lyapunov’s direct method is employed to prove global asymptotic tracking. Simulation results on a three degree-of-freedom(DOF) robot illustrate the effectiveness of the proposed scheme.