Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

This article addresses the reference tracking issue of multi-degree-of-freedom robotic exoskeletons under exogenous perturbations. First, by considering the concept of iteration-dependent full-format dynamic linearization, the exoskeleton robot's dynamics is reformulated as a linear data model in an iterative manner. Then, based upon an iterative sliding variable, a novel data-based model-free adaptive iterative learning integral terminal sliding mode control is designed. The superiority of the proposed study is that the reference tracking problem is just solved by using the measured input/output information of exoskeletons. In addition, its finite-iteration convergence is also affirmed by mathematical analysis. The simulation investigation together with the compared results also clarifies the efficiency of the developed learning-based control algorithm.