Compliant adaptive control of human upper-limb exoskeleton robot with unknown dynamics based on a Modified Function Approximation Technique (MFAT)

Liu

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2021

This article proposes a new adaptive sliding mode repetitive learning control strategy. The proposed controller can obtain satisfactory position tracking performance in the presence of unknown dynamics and external disturbance. The unknown dynamics parameters of the exoskeleton system can be estimated via an adaptive algorithm, which is used to design the sliding mode control law. Besides, the periodic external disturbance of the system can be compensated by repetitive learning to reduce the tracking error. The stability of the proposed method is demonstrated rigorous by the Lyapunov theory. Using an upper-limb exoskeleton model, simulation results demonstrate the effectiveness of the control strategy. The proposed method has a better control performance than other methods.

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode repetitive learning control of the upper-limb exoskeleton with unknown dynamics

Liu

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2021

El-Cezeri Fen Ve Mühendislik Dergisi

“…Recently, promising progress has been made in the field of human-exoskeleton robot interaction [4]. These robots are still an open area of research due to their complex mechanical design, various aid strategies (for different types of patients), and the precision of the mechanism [5]. As a typical physical user-robot interaction, exoskeleton robots have been developed to improve the performance of a healthy user or to provide walking support and walking rehabilitation to users with reduced functionality [4].…”

Section: Introductionmentioning

confidence: 99%

Üst Uzuv Robot Kol Sistemi Tasarımı ve Kinematik Analizi

Ersin¹,

Yaz²,

Gökçe³

2020

In this study, a wearable upper limb robot arm design has been designed for individuals who experience discomfort in the arm muscle and experience power loss. The user with arm discomfort can attach the designed system to her arm and perform daily life activities like healthy individuals. The upper limb robot arm system is designed in Solidworks program. The system is attached to the waist of the user and wraps the entire arm. The designed robot arm system is designed to be produced of 5 cm thick aluminum material. Angular servo motors are mounted from the joints to the wrists and elbows. The linear motor is mounted to the shoulder part of the upper limb system to provide both vertical and horizontal movement. Necessary analyses were done for the mechanism designed with the help of Solidworks software. The safety coefficients of the arm, hand, and shoulder apparatuses were calculated for a maximum load of 250 N. Later, restrictions were made for the necessary movements and the powers and torque values of the motors to be used were calculated. Also, maximum displacements were determined by making force distributions for the arm, hand, and shoulder parts of the system.

“…Exoskeletons have a resemblance to human anatomy and could be actuated by specific methods, whereas robots with end-effectors could be in any configuration. There is some kind of upper-extremity rehabilitation of exoskeleton robots like MAHI Exo-II, ETS-MARS, and CADEN-7 and some form of end-effector like MIT-MANUS and MIME (Krebs et al, 1998 ; Pehlivan et al, 2012 ; Niyetkaliyev et al, 2017 ; Brahmi et al, 2019 ; McDonald et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in Telerehabilitation

et al. 2020

In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a “hands-on” manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices ( L and D ) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve “Simultaneous Training and Therapy” in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.