An artificial systems, computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

In recent years, intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation, wherein the human-robot interaction (HRI) control strategy is a momentous part that needs to be ameliorated. Specially, the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be addressed. To deal with these urgent issues, in this study, artificial systems, computational experiments and a parallel execution intelligent control framework are constructed for the HRI control. The upper limb-robotic exoskeleton system is re-modelled as an artificial system. Depending on surface electromyogram-based subject's active motion intention in the practical system, a non-convex function activated anti-disturbance zeroing neurodynamic (NC-ADZND) controller is devised in the artificial system for parallel interaction and HRI control with the practical system. Furthermore, the linear activation function-based zeroing neurodynamic (LAF-ZND) controller and proportional-derivative (posterior deltoid (PD)) controller are presented and compared. Theoretical results substantiate the global convergence and robustness of the proposed controller in the presence of different external disturbances. In addition, the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

Automated summary

In recent years, intelligent robots have been widely used in the industry and intelligent rehabilitation. The human-robot interaction (HRI) control strategy plays a vital role and needs improvement. This study presents a parallel execution intelligent control framework to address the issues related to HRI control in the presence of unknown external disturbances.