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Hu Y, Meng J, Li G, Zhao D, Feng G, Zuo G, Liu Y, Zhang J, Shi C. Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot. SENSORS (BASEL, SWITZERLAND) 2023; 23:4042. [PMID: 37112385 PMCID: PMC10146308 DOI: 10.3390/s23084042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/07/2023] [Accepted: 04/15/2023] [Indexed: 06/19/2023]
Abstract
Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient's position tracking performance while ignoring the patient's interactive force situation, resulting in the inability to accurately assess the patient's true motor intention and difficulty stimulating the patient's initiative, thus negatively affecting the patient's rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects' task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject's task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject's motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject's initiative. Through experiments, this control strategy has been shown to not only improve the subject's initiative during the training process and ensure their safety during training but also enhance the subject's motor learning ability.
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Affiliation(s)
- Yang Hu
- School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (Y.H.); (J.M.); (D.Z.); (Y.L.)
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Jingyan Meng
- School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (Y.H.); (J.M.); (D.Z.); (Y.L.)
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Guoning Li
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Dazheng Zhao
- School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (Y.H.); (J.M.); (D.Z.); (Y.L.)
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Guang Feng
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Guokun Zuo
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Yunfeng Liu
- School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (Y.H.); (J.M.); (D.Z.); (Y.L.)
| | - Jiaji Zhang
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Changcheng Shi
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo 315300, China; (G.L.); (G.F.); (G.Z.); (J.Z.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
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Wang S, Liu X, Gao M, Chen M, Yung KL, Jiang S. Multi-objective auto-scaling scheduling for micro-service workflows in hybrid clouds. ENTERP INF SYST-UK 2022. [DOI: 10.1080/17517575.2022.2069478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Shijia Wang
- School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China
- School of Management Science and Engineering, Central University of Finance and Economics, Beijing, China
| | - Xuan Liu
- School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China
| | - Ming Gao
- School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China
- Center for Post-doctoral Studies of Computer Science, Northeastern University, Shenyang, China
| | - Mingxia Chen
- School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China
| | - Kai Leung Yung
- Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China
| | - Shancheng Jiang
- School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China
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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.
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