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Improved Cascade Active Disturbance Rejection Control for Functional Electrical Stimulation Based Wrist Tremor Suppression System Considering the Effect of Output Noise

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13456))

Abstract

The wrist tremor suppression system designed based on functional electrical stimulation technology has been welcomed by the majority of tremor patients as a non-invasive rehabilitation therapy. Due to the complex physiological structure characteristics of the wrist musculoskeletal system, it is difficult to accurately model it, and the measurement noise at the output port of the wrist tremor suppression system is difficult to avoid. These problems seriously affect the performance of tremor suppression. In order to solve the above problems, an improved linear active disturbance rejection control (LADRC) scheme is proposed based on the cascade strategy. The simulation results show that the proposed improved cascade LADRC can not only meet the requirements of system control performance against model uncertainty, but also attenuate the influence of output noise on the system, so as to effectively improve the suppression performance of tremor.

Supported by National Natural Science Foundation of China (62103376), Robot Perception and Control Support Program for Outstanding Foreign Scientists in Henan Province of China (GZS2019008), China Postdoctoral Science Foundation (2018M632801) and Science & Technology Research Project in Henan Province of China (212102310253).

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References

  1. Tousi, B., Cummings, J. (eds.): Neuro-Geriatrics. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56484-5

    Book  Google Scholar 

  2. Wen, Y., Huang, X., Tu, X., Huang, M.: Functional electrical stimulation array electrode system applied to a wrist rehabilitation robot. J. Huazhong Univ. Sci. Technol. (Nat. Sci. Edn.) 41(S1), 332–334, 342 (2013). https://doi.org/10.13245/j.hust.2013.s1.086

  3. Copur, E.H., Freeman, C.T., Chu, B., Laila, D.S.: Repetitive control of electrical stimulation for tremor suppression. IEEE Trans. Control Syst. Technol. 27(2), 540–552 (2019). https://doi.org/10.1109/TCST.2017.2771327

    Article  Google Scholar 

  4. Han, J.: From PID to active disturbance rejection control. IEEE Trans. Ind. Electron. 56(3), 900–906 (2009). https://doi.org/10.1109/TIE.2008.2011621

    Article  Google Scholar 

  5. Li, Y., Zhang, C., Song, J., Li, X., Duan, B.: An active disturbance rejection control strategy for a three-phase isolated matrix rectifier. IEEE Trans. Transp. Electrif. 8(1), 820–829 (2021). https://doi.org/10.1109/TTE.2021.3100544

    Article  Google Scholar 

  6. Chen, Z., Ruan, X., Li, Y.: Dynamic modeling of a cubical robot balancing on its corner. Control Decis. 34(6), 1203–1210 (2019). https://doi.org/10.13195/j.kzyjc.2017.1559

  7. Liang, Q., Wang, C., Pan, J., Wen, Y., Wang, Y.: Parameter identification of b0 and parameter tuning law in linear active disturbance rejection control. Control Decis. 30(9), 1691–1695 (2015). https://doi.org/10.13195/j.kzyjc.2014.0943

  8. Gao, Y., Wu, W., Gao, L.: Linear active disturbance rejection control for high-order nonlinear systems with uncertainty. Control Decis. 35(2), 483–491 (2020). https://doi.org/10.13195/j.kzyjc.2018.0550

  9. Gao, Z.: Scaling and bandwidth-parameterization based controller tuning. In: American Control Conference, pp. 4989–4996. Institute of Electrical and Electronics Engineers Inc., Denver (2003). https://doi.org/10.1109/ACC.2003.1242516

  10. Chen, Z., Sun, M., Yang, R.: On the Stability of linear active disturbance rejection control. Acta Automatica Sinica 39(5), 574–580 (2013). https://doi.org/10.3724/SP.J.1004.2013.00574

    Article  MathSciNet  Google Scholar 

  11. Gao, Y., Wu, W., Wang, Z.: Cascaded linear active disturbance rejection control for uncertain systems with input constraint and output noise. Acta Automatica Sinica 48(3), 843–852 (2022). https://doi.org/10.16383/j.aas.c190305

  12. Prasov, A.A., Khalil, H.K.: A nonlinear high-gain observer for systems with measurement noise in a feedback control framework. IEEE Trans. Autom. Control 58(3), 569–580 (2013). https://doi.org/10.1109/TAC.2012.2218063

    Article  MathSciNet  MATH  Google Scholar 

  13. Lee, J., Choi, K., Khalil, H. K.: New implementation of high-gain observers in the presence of measurement noise using stochastic approximation. In: European Control Conference, pp. 1740–1745. Institute of Electrical and Electronics Engineers Inc., Aalborg (2016). https://doi.org/10.1109/ECC.2016.7810542

  14. Teel, A. R.: Further variants of the Astolfi/Marconi high-gain observer. In: American Control Conference, pp. 993–998. Institute of Electrical and Electronics Engineers Inc., Boston (2016). https://doi.org/10.1109/ACC.2016.7525044

  15. Battilotti, S.: Robust observer design under measurement noise with gain adaptation and saturated estimates. Automatica 81, 75–86 (2017). https://doi.org/10.1016/j.automatica.2017.02.008

    Article  MathSciNet  MATH  Google Scholar 

  16. Nair, R.R., Behera, L.: Robust adaptive gain higher order sliding mode observer based control-constrained nonlinear model predictive control for spacecraft formation flying. IEEE/CAA J. Automatica Sinica 5(1), 367–381 (2018). https://doi.org/10.1109/JAS.2016.7510253

    Article  MathSciNet  Google Scholar 

  17. Bo, A.P.L., Poignet, P., Zhang D., Ang, W. T.: FES-controlled co-contraction strategies for pathological tremor compensation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1633–1638. IEEE Computer Society, Louis (2009). https://doi.org/10.1109/IROS.2009.5354397

  18. Liu, Y., Qin, Y., Huo, B., Wu, Z.: Functional electrical stimulation based bicep force control via active disturbance rejection control. In: 5th IEEE International Conference on Advanced Robotics and Mechatronics, pp. 306–311. Institute of Electrical and Electronics Engineers Inc., Shenzhen (2020). https://doi.org/10.1109/ICARM49381.2020.9195304

  19. Yang, X., Chen, J., Li, Y., Zhou, Y.: A method of modulation and evaluation for functional electrical stimulation based on muscle activation properties. J. Fuzhou Univ. (Nat. Sci. Edn.) 47(3), 346–351 (2019)

    Google Scholar 

  20. Alibeji, N., Kirsch, N., Farrokhi, S., Sharma, N.: Further results on predictor-based control of neuromuscular electrical stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1095–1105 (2015). https://doi.org/10.1109/TNSRE.2015.2418735

    Article  Google Scholar 

  21. Colacino, F.M., Emiliano, R., Mace, B.R.: Subject-specific musculoskeletal parameters of wrist flexors and extensors estimated by an EMG-driven musculoskeletal model. Med. Eng. Phys. 34(5), 531–540 (2012). https://doi.org/10.1016/j.medengphy.2011.08.012

    Article  Google Scholar 

  22. Yuan, D., Ma, X., Zeng, Q., Qiu, X.: Research on frequency-band characteristics and parameters configuration of linear active disturbance rejection control for second-order systems. Control Theor. Appl. 30(12), 1630–1640 (2004). https://doi.org/10.7641/CTA.2013.30424

    Article  Google Scholar 

  23. Li, H., Zhu, X.: On parameters tuning and optimization of active disturbance rejection controller. Control Eng. China 11(5), 419–423 (2004)

    Google Scholar 

  24. Lin, F., Sun, H., Zheng, Q., Xia, Y.: Novel extended state observer for uncertain system with measurement noise. Control Theor. Appl. 22(6), 995–998 (2005)

    MATH  Google Scholar 

  25. Li, G., Pan, L., Hua, Q., Sun, L., Lee, K.Y.: Water Pump Control: A hybrid data-driven and model-assisted active disturbance rejection approach. Water (Switz.) 11(5), 1066 (2019). https://doi.org/10.3390/w11051066

    Article  Google Scholar 

  26. Lenz, F.A., Jaeger, C.J., Seike, M.S., Lin, Y., Reich, S.G.: Single-neuron analysis of human thalamus in patients with intention tremor and other clinical signs of cerebellar disease. J. Neurophysiol. 87(4), 2084–2094 (2002). https://doi.org/10.1152/jn.00049.2001

    Article  Google Scholar 

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Correspondence to Zan Zhang .

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Tao, C., Zhang, Z., Huo, B., Liu, Y., Li, J., Yu, H. (2022). Improved Cascade Active Disturbance Rejection Control for Functional Electrical Stimulation Based Wrist Tremor Suppression System Considering the Effect of Output Noise. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_21

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  • DOI: https://doi.org/10.1007/978-3-031-13822-5_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-13822-5

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