Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Yang, Qianqian; Xie, Chenyang; Tang, Rongrong; Liu, Huihua; Song, Rong

doi:10.1109/access.2020.3033301

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…

represents the Jacobian matrix of CDRR. The details of calculation process are described in equation (Yang et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have shown that the robot with a compliant control algorithm performs better rehabilitation training for its natural human-robot interaction (HRI) (Niu et al, 2017 ; Yang et al, 2020 ). Since pure force or position control, like that exhibited by the Proportion-Integration-Differentiation (PID) control algorithm (Rosati et al, 2007 ), has proved inadequate for compliant interaction, Hogan ( 1985 ) have proposed an impedance control method which took into account the mechanical interaction between systems and the environment (Veneman et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

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Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task

2022

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To obtain an anthropomorphic performance in physical human-robot interaction during a reaching task, a variable impedance control (vIC) algorithm with human-like characteristics is proposed in this article. The damping value of the proposed method is varied with the target position as well as through the tracking error. The proposed control algorithm is compared with the impedance control algorithm with constant parameters (IC) and another vIC algorithm, which is only changed with the tracking error (vIC-e). The different control algorithms are validated through the simulation study, and are experimentally implemented on a cable-driven rehabilitation robot. The results show that the proposed vIC can improve the tracking accuracy and trajectory smoothness, and reduce the interaction force at the same time.

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“…

represents the Jacobian matrix of CDRR. The details of calculation process are described in equation (Yang et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task

2022

Self Cite

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“…A hybrid active control approach with human intention detection was proposed by Yang et al [94]. To continually analyze the goal location and velocity of the human intention, the human upperlimb model and the minimal jerk model were utilized.…”

Section: Control Laws Used With Motion Intention Estimation In Power ...mentioning

confidence: 99%

Power Assist Rehabilitation Robot and Motion Intention Estimation

Adeola-Bello¹,

Azlan²

2022

IJRCS

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This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot. In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

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“…The impedance control and admittance control algorithms proposed by Hogan et al [21] use the relationship between the force and the position of the robot end to adjust the dynamic impedance characteristics of the system so that the system can express external compliance. Yang et al [22] proposed a hybrid active control algorithm combined with human motion intention and verified the effectiveness of the control algorithm based on the human upper limb model and the minimum continuous motion estimation model. Li et al [23] proposed a variable admittance control method based on human intentions, environmental forces, and environmental stiffness to address physical human-robot interaction (pHRI) coupled to environments of unknown stiffness.…”

Section: Introductionmentioning

confidence: 99%

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

2022

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Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

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Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Cited by 9 publications

References 45 publications

Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task

Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task

Power Assist Rehabilitation Robot and Motion Intention Estimation

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

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