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Tong et al. Intell Robot 2024;4:125-45 Intelligence & Robotics
DOI: 10.20517/ir.2024.08

Research Article Open Access

A novel zero-force control framework for post-stroke
rehabilitation training based on fuzzy-PID method

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Lina Tong , Decheng Cui , Chen Wang , Liang Peng 2
1 School of Artificial Intelligence, China University of Mining and Technology, Beijing 100083, China.
2 State Key Laboratory of Multimodal Artifcial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
100090, China.
Correspondence to: Dr. Chen Wang, Dr. Liang Peng, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of
Automation, Chinese Academy of Sciences, 52 Sanlihe Rd., Xicheng District, Beijing 100190, China. E-mail: wangchen2016@iaac.cn;
liang.peng@ia.ac.cn
How to cite this article: Tong L, Cui D, Wang C, Peng L. A novel zero-force control framework for post-stroke rehabilitation training
based on fuzzy-PID method. Intell Robot 2024;4(1):125-45. http://dx.doi.org/10.20517/ir.2024.08
Received: 1 Dec 2023 First Decision: 15 Jan 2024 Revised: 8 Feb 2024 Accepted: 23 Feb 2024 Published: 21 Mar 2024
Academic Editor: Simon X. Yang Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
As the number of people with neurological disorders increases, movement rehabilitation becomes progressively im-
portant, especially the active rehabilitation training, which has been demonstrated as a promising solution for im-
proving the neural plasticity. In this paper, we developed a 5-degree-of-freedom rehabilitation robot and proposed a
zero-force control framework for active rehabilitation training based on the kinematics and dynamics identification.
According to the robot motion characteristics, the fuzzy PID algorithm was designed to further improve the flexibil-
ity of the robot. Experiments demonstrated that the proposed control method reduced the Root Mean Square Error
and Mean Absolute Error evaluation indexes by more than 15% on average and improves the coefficient of determi-
nation ( ) by 4% compared with the traditional PID algorithm. In order to improve the active participation of the
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post-stroke rehabilitation training, this paper designed an active rehabilitation training scheme based on gamified
scenarios, which further enhanced the efficiency of rehabilitation training by means of visual feedback.

Keywords: Upper limb exoskeleton rehabilitation robot, rehabilitation, zero force control, fuzzy control, virtual reality

1. INTRODUCTION
With the ageing of the population in society, the number of elderly people with movement disorders caused by
[1]
stroke, spinal cord injury, traumatic brain injury, and deterioration of limb function is increasing . Patients

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