Adaptive Sliding-mode Iterative Learning Control for 2R1T Parallel Robots

To solve the tracking accuracy problem in repetitive machining of parallel robots with 2 rotational and 1 translational (2R1T) degrees of freedom (DOFs), an adaptive sliding-mode iterative learning control scheme is proposed. Specifically, an iterative learning control method is adopted to improve the trajectory control accuracy, a sliding-mode control algorithm is introduced to enhance the robustness of the control system, and the adaptive control method is employed to deal with the intrinsic uncertainty of the control, therefore, overcoming the oscillation problem caused by switching terms in sliding-mode control. Furthermore, the convergence of the proposed control system is proved by Bellman-Gronwall theorem and \lambda -norm. The stability of the proposed control system is validated through designing a Lyapunov-like function. Finally, numerical simulations and prototype experiments are carried out to verify the proposed control scheme. The experimental results show that the proposed control scheme can significantly improve the control accuracy and the system robustness of the 2R1T parallel robots. Under this control algorithm, the mean error, maximum error and standard deviation of the joint space of the parallel robot are reduced by 59.5\%, 55.1\% and 60.1\% compared with the PD (proportional-derivative) control method.