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

Published online by Cambridge University Press:  03 August 2022

Karam Almaghout ,
Bahram Tarvirdizadeh Open the ORCID record for Bahram Tarvirdizadeh [Opens in a new window] ,
Khalil Alipour Open the ORCID record for Khalil Alipour [Opens in a new window]  and
Alireza Hadi Open the ORCID record for Alireza Hadi [Opens in a new window]
Karam Almaghout
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran Institute of Robotics and Computer Vision, Innopolis University, Innopolis, Russia
Bahram Tarvirdizadeh*
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Khalil Alipour
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Alireza Hadi
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
*
*Corresponding author. E-mail: bahram@ut.ac.ir
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Abstract

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.

Keywords

rehabilitation robotics systemsliding mode controlradial basis functioninteraction torquesmotion recovery
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4512 - 4534
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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