Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric

Taherifar, Ali; Vossoughi, Gholamreza; Ghafari, Ali Selk

doi:10.1017/s0263574717000480

Cited by 20 publications

(12 citation statements)

References 23 publications

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“…Xu et al [34] proposed an adaptive impedance controller for a therapeutic robot based on hybrid learning method. Taherifar et al [35] designed a variable admittance control scheme to reduce the human-robot interaction energy of a gait rehabilitation exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Chen

2019

IEEE Access

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Robot-assisted rehabilitation therapy has become an important technology applied to recover the motor functions of disabled individuals. In the present paper, an adaptive admittance control strategy combined with a neural-network-based disturbance observer (AACNDO) is developed for a therapeutic robot to provide upper extremity movement assistance. Firstly, a comprehensive overview of the robot hardware and real-time control system is introduced. Then, the dynamics-based adaptive admittance controller is designed to improve human-robot interaction compliance and induce the active participation of the patients during rehabilitation training. A disturbance observer with a radial basis function network is designed to guarantee the control performance with external uncertainties and dynamics error. Besides, an adaption law is integrated into the admittance model to adjust the interaction compliance in different working areas based on the motion intention and recovery phase of the patient. Further experimental investigations, including sinusoidal trajectory tracking experiments, circular trajectory tracking experiments with admittance adjustment, and intention-based resistive training experiments, are conducted by three volunteers. Finally, the experimental results validate the feasibility and effectiveness of the rehabilitation robot and AACNDO scheme in providing patient-passive and patient-cooperative rehabilitation training.

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Section: Introductionmentioning

confidence: 99%

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Chen

2019

IEEE Access

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“…Herein, we introduce a control law that consists of two terms, an equivalent and auxiliary, the former considers the known term while the latter tries to vanish the impact of the unknown term. The control law is τ = τ eq + τ aux (26) making ṡ = 0, and neglecting the unknown term D(t), the equivalent control law τ eq is obtained as…”

Section: Adaptive Sliding Mode Controlmentioning

confidence: 99%

“…In rehabilitation robots, admittance control has been implemented to enable the participation of the patient during the exercise. This participation is mainly required for therapy stages, where the patient can control their limb, but they are still weak to move independently [25,26].…”

Section: Introductionmentioning

confidence: 99%

RBF neural network-based admittance PD control for knee rehabilitation robot

et al. 2022

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Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.

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“…More control strategies for rehabilitation robots support this point of view. [17][18][19][20][21][22][23][24][25] In this article, we proposed a reward-punishment feedback control strategy to promote subjects' engagement for wrist rehabilitation. Firstly, we utilize the energy contributed by the subject to estimate his/her engagement.…”

Section: Control Of Rehabilitation Robots However Remains Anmentioning

confidence: 99%

A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Wang

Zhang

Zuo

et al. 2020

International Journal of Advanced Robotic Systems

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Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

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Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric

Cited by 20 publications

References 23 publications

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

RBF neural network-based admittance PD control for knee rehabilitation robot

A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

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