Adaptive neural tracking control for upper limb rehabilitation robot with output constraints

The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot,an adaptive neural full-state feedback control is proposed.The neural network is utilised to approximate the dy-namics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient.By incorporating a high-gain observer,unmeasurable state information is integrated into the output feedback control.Taking into consider-ation the issue of joint position constraints during the actual rehabilitation training process,an adaptive neural full-state and output feedback control scheme with output constraint is further designed.From the perspective of safety in human–robot interaction during rehabilitation training,log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region.The stability of the closed-loop system is proved by Lyapunov stability theory.The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.

Safety Protection Method of Rehabilitation Robot Based on fNIRS and RGB-D Information Fusion

In order to improve the safety protection performance of the rehabilitation robot,an active safety protection method is proposed in the rehabilitation scene.The oxyhemoglobin concentration information and RGB-D information are combined in this method,which aims to realize the comprehensive monitoring of the invasion target,the patient’s brain function movement state,and the joint angle in the rehabilitation scene.The main focus is to study the fusion method of the oxyhemoglobin concentration information and RGB-D information in the rehabilitation scene.Frequency analysis of brain functional connectivity coefficient was used to distinguish the basic motion states.The human skeleton recognition algorithm was used to realize the angle monitoring of the upper limb joint combined with the depth information.Compared with speed and separation monitoring,the protection method of multi-information fusion is safer and more comprehensive for stroke patients.By building the active safety protection platform of the upper limb rehabilitation robot,the performance of the system in different safety states is tested,and the safety protection performance of the method in the upper limb rehabilitation scene is verified.

Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction

This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities.The first step is to estimate the human's target position(also known as TPH).The second step is to develop a robust adaptive impedance control mechanism.A novel Non-singular Terminal Sliding Mode Control combined with an adaptive super-twisting controller is being developed to achieve this goal.This combination's purpose is to provide high reliability,continuous performance tracking of the system's trajectories.The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering,which is the sliding mode's most glaring issue.The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network,which allows a robotic exoskeleton to simply track the desired impedance model.To validate the approach in real-time,an exoskeleton robot was deployed in controlled experimental circumstances.A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.