Lower extremity robotic exoskeletons(LEEX)can not only improve the ability of the human body but also provide healing treatment for people with lower extremity dysfunction.There are a wide range of application needs a...Lower extremity robotic exoskeletons(LEEX)can not only improve the ability of the human body but also provide healing treatment for people with lower extremity dysfunction.There are a wide range of application needs and development prospects in the military,industry,medical treatment,consumption and other felds,which has aroused widespread concern in society.This paper attempts to review LEEX technical development.First,the history of LEEX is briefy traced.Second,based on existing research,LEEX is classifed according to auxiliary body parts,structural forms,functions and felds,and typical LEEX prototypes and products are introduced.Then,the latest key technologies are analyzed and summarized,and the research contents,such as bionic structure and driving characteristics,human–robot interaction(HRI)and intentawareness,intelligent control strategy,and evaluation method of power-assisted walking efciency,are described in detail.Finally,existing LEEX problems and challenges are analyzed,a future development trend is proposed,and a multidisciplinary development direction of the key technology is provided.展开更多
The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore ...The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured.The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program.In this study,a radial basis function(RBF)neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskel-eton robot.A realistic friction model is used for modelling joint friction.High trajectory tracking accuracies have been obtained.Evidence of computational efficiency has been observed.The stability analysis of the developed controller is presented.Analysis of variance is used to assess the controller's resilience to parameter variation.To show the effectiveness of the developed controller,a comparative study was performe between the developed RBF network‐based controller and Sliding Mode Controller,Computed Tor-que Controller,Adaptive controller,Linear Quadratic Regulator and Model Reference Computed Torque Controller.展开更多
基金the National Basic Research Program of China(Grant No.2016YFC0802700)the National Natural Science Foundation of China(Grant No.50975010,Grant No.51075017).
文摘Lower extremity robotic exoskeletons(LEEX)can not only improve the ability of the human body but also provide healing treatment for people with lower extremity dysfunction.There are a wide range of application needs and development prospects in the military,industry,medical treatment,consumption and other felds,which has aroused widespread concern in society.This paper attempts to review LEEX technical development.First,the history of LEEX is briefy traced.Second,based on existing research,LEEX is classifed according to auxiliary body parts,structural forms,functions and felds,and typical LEEX prototypes and products are introduced.Then,the latest key technologies are analyzed and summarized,and the research contents,such as bionic structure and driving characteristics,human–robot interaction(HRI)and intentawareness,intelligent control strategy,and evaluation method of power-assisted walking efciency,are described in detail.Finally,existing LEEX problems and challenges are analyzed,a future development trend is proposed,and a multidisciplinary development direction of the key technology is provided.
文摘The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured.The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program.In this study,a radial basis function(RBF)neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskel-eton robot.A realistic friction model is used for modelling joint friction.High trajectory tracking accuracies have been obtained.Evidence of computational efficiency has been observed.The stability analysis of the developed controller is presented.Analysis of variance is used to assess the controller's resilience to parameter variation.To show the effectiveness of the developed controller,a comparative study was performe between the developed RBF network‐based controller and Sliding Mode Controller,Computed Tor-que Controller,Adaptive controller,Linear Quadratic Regulator and Model Reference Computed Torque Controller.
