Citation: Feng, G.; Zhang, J.; Zuo, G.;
Li, M.; Jiang, D.; Yang, L. Dual-Modal
Hybrid Control for an Upper-Limb
Rehabilitation Robot. Machines 2022,
10, 324. https://doi.org/10.3390/
machines10050324
Academic Editor: Davide Astolfi
Received: 21 March 2022
Accepted: 20 April 2022
Published: 29 April 2022
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Article
Dual-Modal Hybrid Control for an Upper-Limb
Rehabilitation Robot
Guang Feng
1,2,3
, Jiaji Zhang
2,3,
* , Guokun Zuo
2,3,
* , Maoqin Li
2,3
, Dexin Jiang
1,2,3
and Lei Yang
1
1
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
fengguang@nimte.ac.cn (G.F.); jiangdexin@nimte.ac.cn (D.J.); yanglei130@shu.edu.cn (L.Y.)
2
Ningbo Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering,
Chinese Academy of Sciences, Cixi 315300, China; limaoqin@nimte.ac.cn
3
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,
Ningbo 315201, China
* Correspondence: zhangjiaji@nimte.ac.cn (J.Z.); moonstone@nimte.ac.cn (G.Z.)
Abstract:
The recovery treatment of motor dysfunction plays a crucial role in rehabilitation therapy.
Rehabilitation robots are partially or fully replacing therapists in assisting patients in exercise by
advantage of robot technologies. However, the rehabilitation training system is not yet intelligent
enough to provide suitable exercise modes based on the exercise intentions of patients with different
motor abilities. In this paper, a dual-modal hybrid self-switching control strategy (DHSS) is proposed
to automatically determine the exercise mode of patients, i.e., passive and assistive exercise mode.
In this strategy, the potential field method and the ADRC position control are employed to plan
trajectories and assist patients’ training. Dual-modal self-switching rules based on the motor and
impulse information of patients are presented to identify patients’ motor abilities. Finally, the DHSS
assisted five subjects in performing the training with an average deviation error of less than 2 mm in
both exercise modes. The experimental results demonstrate that the muscle activation of the subjects
differed significantly in different modes. It also verifies that DHSS is reasonable and effective, which
helps patients to train independently without therapists.
Keywords: rehabilitation robot; potential field; dual-modal switching; human-robot interaction
1. Introduction
Rehabilitation is essential for people with impaired motor function due to age-associated
diseases or accidents, in order to fully or partially restore the motor function of their
limbs [
1
]. Due to the shortage of therapists and the cost of various rehabilitation expenses,
a great deal of research has been conducted on robot-assisted rehabilitation. Researchers
have applied robotics to the field of upper limb rehabilitation and have developed a variety
of devices. Examples include end-traction devices: MIT-MANUS [
2
], MIME [
3
], EULRR [
4
],
and BULReD [
5
]; and exoskeletal rehabilitation devices: ARMin [
6
] and UL-EXO7 [
7
–
9
].
These rehabilitation robots can provide multiple modes of rehabilitation exercises: passive
exercise, assistive exercise, and resistance exercise.
The human–robot interaction control strategy of the rehabilitation robots is also a
key factor affecting the rehabilitation results. The patient’s initial rehabilitation focuses on
unidirectional master–slave passive exercise; and upper limb rehabilitation robots generally
use position control strategies such as classical PID control, sliding mode control [
10
], and
active disturbance rejection control (ADRC) [
11
]. In addition, passive exercise puts high
demands on precision and safe motion planning. Thus, a motion planning strategy with
minimal potential energy modulation has been proposed [
12
]. However, passive exercise
strictly follows the physician’s pre-defined trajectory without any form of interaction
between patients and rehabilitation robots during the whole training process [
13
]. Patients
will eventually lose active participation in passive exercise. Research findings [
14
] in
Machines 2022, 10, 324. https://doi.org/10.3390/machines10050324 https://www.mdpi.com/journal/machines
