This article proposes a new adaptive sliding mode repetitive learning control strategy. The proposed controller can obtain satisfactory position tracking performance in the presence of unknown dynamics and external disturbance. The unknown dynamics parameters of the exoskeleton system can be estimated via an adaptive algorithm, which is used to design the sliding mode control law. Besides, the periodic external disturbance of the system can be compensated by repetitive learning to reduce the tracking error. The stability of the proposed method is demonstrated rigorous by the Lyapunov theory. Using an upper-limb exoskeleton model, simulation results demonstrate the effectiveness of the control strategy. The proposed method has a better control performance than other methods.

本文提出了一种新的自适应滑模重复学习控制策略。所提出的控制器在存在未知动力学和外部干扰的情况下可以获得令人满意的位置跟踪性能。外骨骼系统的未知动力学参数可以通过自适应算法来估计，该算法用于设计滑模控制律。此外，系统的周期性外部干扰可以通过重复学习来补偿，以减少跟踪误差。李雅普诺夫理论严格证明了所提方法的稳定性。使用上肢外骨骼模型，仿真结果证明了该控制策略的有效性。所提出的方法具有比其他方法更好的控制性能。