Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control

Jiang, Xuepeng; Huang, Xinhan; Chen, Xiong; Sun, Ruiying; Xiong, Yan

doi:10.1115/1.4005436

Cited by 21 publications

(14 citation statements)

References 8 publications

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“…During experiment, the experimenter was instructed to grasp the robot end-effector with his/her right hand and, moreover, intentionally apply interaction forces with certain range to adjust the training path during trajectory tracking process. The results of the experiment conducted by S1 are shown in Figs (27) where i (t) denotes the ith resultant interaction force data.…”

Section: B Impedance-based Trajectory Tracking Experimentsmentioning

confidence: 99%

“…Currently, the existing rehabilitation control schemes can be classified into two types according to the participation degree of patients, i.e., the passive training control strategies [19]- [21] for the patients at the acute period to passively conduct repetitive movement tasks along predefined trajectory, and the cooperative training control strategies [22]- [26] for the patients at the recovery period to be actively engaged in the therapy training program. In [27], a passive training control approach combined with neuron proportion-integral and feedforward compensation control was developed to reduce the trajectory tracking error of a pneumatic muscles-driven rehabilitation robot. In [28], an enhanced neural-network-based repetitive learning controller was proposed for a lower limb exoskeleton to improve control accuracy and human safety during trajectory tracking training.…”

Section: Introductionmentioning

confidence: 99%

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Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

Dawen

Chen

et al. 2019

IEEE Access

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Robot-assisted cooperative rehabilitation training has shown superiority in helping the individuals with motion impairment problems to regain their motor functions. This paper presents the development and evaluation of a new cooperative training control scheme for an end-effector-type rehabilitation robot to provide upper extremity rehabilitation training with desired compliance and intensity. Firstly, the overall mechanical structure and real-time control system of rehabilitation robot are introduced. Secondly, an integral fuzzy sliding mode impedance control strategy combined with time-delay estimation (IFSMIC-TDE) is proposed to induce the active participation of patients and suppress impedance error. The IFSMIC-TDE approach is free from nonlinear robot dynamics, and it is designed to be robust to the external uncertainties and inherent chattering characteristics. After that, the closed-loop system stability with IFSMIC-TDE is proved based on the Lyapunov stability theory. Finally, experimental investigations are conducted to illustrate the effectiveness of the proposed rehabilitation robot and control algorithm. The comparison results indicate that the proposed IFSMIC-TDE can achieve better control performance and less impedance error. Besides, the interaction compliance and training intensity can be qualitatively adjusted via appropriate impedance parameters. INDEX TERMS Upper extremity rehabilitation robot, cooperative training, sliding mode impedance control, fuzzy turner, time delay estimation.

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Section: B Impedance-based Trajectory Tracking Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

Dawen

Chen

et al. 2019

IEEE Access

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“…The control of the human–robot interaction system is a great challenge due to its highly nonlinear characteristics. Many control algorithms have been proposed to enhance the tracking accuracy of passive training, such as the robust adaptive neural controller ( 29 ), fuzzy adaptive backstepping controller ( 30 ), neural proportional–integral–derivative (PID) controller ( 31 ), fuzzy sliding mode controller ( 32 ), and neuron PI controller ( 33 ).…”

Section: Introductionmentioning

confidence: 99%

Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training

Wang

Chen

et al. 2018

Front. Neurol.

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Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.

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“…However, it is a challenge to guarantee the position control accuracy during rehabilitation training due to the highly nonlinear properties and unexpected uncertainties of human-robot interaction. Different kinds of control algorithms have been developed to improve control performance of patient-passive training, including neural proportional-integral-derivative (PID) control [ 23 ], neural proportional-integral (PI) control [ 24 ], adaptive nonsingular terminal sliding mode control (SMC) [ 25 ], disturbance observer-based fuzzy control [ 26 ], neural-fuzzy adaptive control [ 27 ], adaptive backlash compensation control [ 28 ], and so on. Comparatively, the patient-cooperation training is applicable for the patients at the comparative recovery period, who have regained parts of motor functions.…”

Section: Introductionmentioning

confidence: 99%

Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

2018

Sensors

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Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

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Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control

Cited by 21 publications

References 8 publications

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training

Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

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