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A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

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Abstract

This article presents a novel adaptive iterative learning control (AILC), and designs a human-in-loop control pattern (HIL-CP), which simulates the proposed approach using different lower limb rehabilitation robot models. The stability of the AILC controller is proposed and verified via a Lyapunov-like function, where novel controller shows strong robustness in disturbances environment. Based on AILC, the core of the HIL-CP interactive control mode is to estimate the human surface electromyography by neural network model and get the real-time desired trajectory to iterate out the optimal actual tracking trajectory, which reduce the tracking error quickly and ensure the rehabilitation training effect of patients. Furthermore, the MATLAB software is employed to conduct simulation experiments the proposed approach. The simulation results show that the HIL-CP is highly efficient and rapidly convergent in a satisfied degree. The angle error is \({\mathrm{{0.25}}^\text {o}}\pm {\mathrm{{0.2}}^\text {o}} \) for patients and \({\mathrm{{0.03}}^\text {o}}\pm {\mathrm{{0.02}}^\text {o}} \) for healthy people. Compared with the existing sliding mode controller, it is proven that the AILC controller is much more effective and noise-tolerant ability in the presence of bounded nonlinear disturbance.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the Technical Editor for their valuable comments and suggestions on revising this paper.

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Authors and Affiliations

  1. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Zhongbo Sun

  2. Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun, 130025, China

    Zhongbo Sun

  3. Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Feng Li

  4. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China

    Feng Li

  5. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Yufeng Lian, Shuaishi Liu & Keping Liu

  6. The Second Hospital Norman Bethune of Jilin University, Changchun, 130000, China

    Xiaoqin Duan

  7. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Long Jin

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  1. Zhongbo Sun
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  2. Feng Li
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  3. Xiaoqin Duan
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  4. Long Jin
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  5. Yufeng Lian
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Correspondence to Keping Liu.

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The work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61873304, 11701209 and 51875047, and also in part by the China Postdoctoral Science Foundation Funded Project under Grant Nos. 2018M641784, 2019T120240 and also in part by the Key Science and Technology Projects of Jilin Province, China, Grant Nos.20200201291JC,and also in part by the Fundamental Research Funds for the Central Universities (No. lzujbky-2019-89)

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Sun, Z., Li, F., Duan, X. et al. A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment. Auton Robot 45, 595–610 (2021). https://doi.org/10.1007/s10514-021-09988-3

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  • DOI: https://doi.org/10.1007/s10514-021-09988-3

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