Control Strategy for Power Assist Upper Limb Rehabilitation Robot with the Therapist’s Motion Intention Prediction
DOI:
https://doi.org/10.31436/iiumej.v24i1.2604Keywords:
Upper Limb rehabilitation, Motion intention estimator, uncertainties, therapist assistance, rehabilitation robotAbstract
Currently, fully automated rehabilitation robots can assist therapists in providing rehabilitation therapy, hence the patients could get hurt. On the other hand, manual treatment may cause less patient injury but it is tiresome, and there are not enough therapists in most countries. Power assist rehabilitation robots can support the therapists in conducting the treatment and may help to alleviate this problem. The goal of this study is to develop a control strategy for the robot to assist the therapist’s movement in a power assist upper limb rehabilitation treatment. The system combines the advantages of robotic and manual rehabilitation therapy. Torque and position sensors fitted on the power assist upper limb rehabilitation robot arm are used for motion intention estimation. The amount of angular velocity necessary to be delivered to the feedback controller will be determined by predicting the therapist‘s motion intention using the impedance control method. The resulting velocity from the motion intention estimator is incorporated into the Sliding Mode Control - Function Approximation Technique (SMC-FAT) based adaptive controller. The SMC-FAT based adaptive controller in the feedback loop, overcomes the uncertain parameters in the combination of the robot and the human arm. The motion intention estimator forecasts the movement of therapists. The proposed controller is used to regulate elbow flexion and extension motion on a power assist upper limb rehabilitation robot with one degree of freedom (DOF). The proposed control system has been tested using MATLAB simulation and hardware experimental tests. The outcomes demonstrate the effectiveness of the proposed controller in directing the rehabilitation robot to follow the desired trajectory based on the therapist's motion intention, with maximum errors of 0.002rad/sec, 0.005rad/sec and 0.02rad/sec for sinusoidal, constant torque values, and hardware experiment respectively.
ABSTRAK: Pada masa ini, robot rehabilitasi automatik sepenuhnya dapat membantu ahli terapi dalam menyediakan terapi pemulihan, tetapi pesakit berkemungkinan tercedera. Sebaliknya, rawatan manual berkemungkinan mengurangkan kecederaan pesakit tetapi ia memenatkan, dan terdapat kurang ahli terapi yang mencukupi di kebanyakan negara. Robot pembantu rehabilitasi dapat membantu ahli terapi dalam menjalankan pemulihan dan mengurangkan masalah ini. Sistem ini menggabungkan kelebihan terapi pemulihan robotik dan manual. Alat pengesan tork dan kedudukan diletakkan pada anggota atas lengan robot rahabilitasi yang digunakan bagi mengesan anggaran jarak pergerakan ahli terapi. Anggaran halaju sudut diperlukan bagi kawalan gerak balas dan dapat diketahui melalui anggaran niat gerakan ahli terapi menggunakan kaedah kawalan impedans. Halaju yang terhasil daripada anggaran niat gerakan diadaptasi ke dalam pengawal adaptif berasaskan Kawalan Mod Gelongsor - Teknik Anggaran Fungsi (SMC-FAT). Pengawal penyesuaian berasaskan SMC-FAT dalam gelung maklum balas, mengatasi parameter yang tidak pasti dalam gabungan robot dan lengan manusia. Penganggar niat gerakan meramalkan gerakan ahli terapi. Pengawal yang dicadangkan digunakan bagi mengawal lenturan siku dan gerakan lanjutan pada robot rehabilitasi dengan satu darjah kebebasan (DOF). Sistem kawalan yang dicadangkan telah diuji menggunakan simulasi MATLAB dan ujian eksperimen perkakasan. Dapatan kajian menunjukkan keberkesanan pengawal yang dicadangkan dalam mengarahkan robot rehabilitasi mengikut trajektori yang dikehendaki berdasarkan niat gerakan ahli terapi, dengan ralat maksimum masing-masing 0.002rad/s dan 0.005rad/s bagi sinusoidal, nilai tork malar, dan eksperimen perkakasan masing-masing.
Downloads
Metrics
References
Eiammanussakul, T., and Sangveraphunsiri, V. (2018). A lower limb rehabilitation robot in sitting position with a review of training activities. Journal of Healthcare Engineering, 2018, 1–18. DOI: https://doi.org/10.1155/2018/1927807
Bogue, R. (2018). Rehabilitation robots. Industrial Robot, 45(3), 301–306. DOI: https://doi.org/10.1108/IR-03-2018-0046
Alrabghi, L., Alnemari,R., Aloteebi, R., Alshammari, H., Ayyad, M., Al Ibrahim,M., Alotayfi, M., Bugshan, T., Alfaifi, A., and Aljuwayd, H. (2018). Stroke types and management. International Journal Of Community Medicine And Public Health, 5(9), 3715. DOI: https://doi.org/10.18203/2394-6040.ijcmph20183439
Tang, Z., Zhang, K., Sun, S., Gao, Z., Zhang, L., and Yang, Z. (2014). An upper-limb power-assist exoskeleton using proportional myoelectric control. Sensors (Switzerland), 14(4), 6677–6694. DOI: https://doi.org/10.3390/s140406677
Kadota, K., Akai, M., Kawashima, K., and Kagawa, T. (2009). Development of power-assist robot arm using pneumatic rubber muscles with a balloon sensor. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 546–551. DOI: https://doi.org/10.1109/ROMAN.2009.5326335
Liu, Z., and Hao, J. (2019). Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network. Journal of Robotics, 2019, 1–8. DOI: https://doi.org/10.1155/2019/4141269
Lee, J., Kim, M., Ko, H., and Kim, K. (2014). A control method of power-assisted robot for upper limb considering intention-based motion by using sEMG signal. 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014, 385–390. DOI: https://doi.org/10.1109/URAI.2014.7057374
Huang, J., Huo, W., Xu, W., Mohammed, S., and Amirat, Y. (2015). Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition. IEEE Transactions on Automation Science and Engineering, 12(4), 1257–1270. DOI: https://doi.org/10.1109/TASE.2015.2466634
Li, M., Deng, J., Zha, F., Qiu, S., and Wang, X. (2018). Motion Intention Estimation for Active Power-Assist Lower Limb Exoskeleton Robot ( APAL ). Preprints, 1–20. DOI: https://doi.org/10.20944/preprints201806.0149.v1
Chathuramali, K. G. M., and Kiguchi, K. (2020). Real-time detection of the interaction between an upper-limb power-assist robot user and another person for perception-assist. Cognitive Systems Research, 61, 53–63. DOI: https://doi.org/10.1016/j.cogsys.2020.01.002
Struk, S., Correia, N., Guenane, Y., Revol, M., and Cristofari, S. (2018). Full-thickness skin grafts for lower leg defects coverage: Interest of postoperative immobilization. Annales de Chirurgie Plastique Esthetique, 63(3), 229–233. DOI: https://doi.org/10.1016/j.anplas.2017.08.003
Wang, X., Li, X., and Wang, J. (2015). Modeling and identification of the human-exoskeleton interaction dynamics for upper limb rehabilitation. Lecture Notes in Electrical Engineering, 338, 51–60. DOI: https://doi.org/10.1007/978-3-662-46466-3_6
Lee, J., Kim, M., and Kim, K. (2017). A control scheme to minimize muscle energy for power assistant robotic systems under unknown external perturbation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(12), 2313–2327. DOI: https://doi.org/10.1109/TNSRE.2017.2723609
Nomura, S., Takahashi, Y., Sahashi, K., Murai, S., Taniai, Y., and Naniwa, T. (2019). Power assist control based on human motion estimation using motion sensors for powered exoskeleton without binding legs. Applied Sciences (Switzerland), 9(1), 14–16. DOI: https://doi.org/10.3390/app9010164
Zhuang, Y., Yao, S., Ma, C., and Song, R. (2019). Admittance Control Based on EMG-Driven Musculoskeletal Model Improves the Human-Robot Synchronization. IEEE Transactions on Industrial Informatics, 15(2), 1211–1218. DOI: https://doi.org/10.1109/TII.2018.2875729
Wang, W., Zhang, J., Kong, D., Su, S., Yuan, X., & Zhao, C. (2022). Research on control method of upper limb exoskeleton based on mixed perception model. Robotica, 1–17. DOI: https://doi.org/10.1017/S0263574722000480
Wang, W., Qin, L., Yuan, X., Ming, X., Sun, T., and Liu, Y. (2019). Bionic control of exoskeleton robot based on motion intention for rehabilitation training. Advanced Robotics, 33(12), 590–601. DOI: https://doi.org/10.1080/01691864.2019.1621774
Yang, C., Chen, C., He, W., Cui, R., and Li, Z. (2019). Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives. IEEE Transactions on Neural Networks and Learning Systems, 30(3), 777–787. DOI: https://doi.org/10.1109/TNNLS.2018.2852711
Shanta, M. N. T., and Azlan, N. Z. (2015). Function Approximation Technique based Sliding Mode Controller Adaptive Control of Robotic Arm with Time-Varying Uncertainties. Procedia Computer Science, 76, 87–94. DOI: https://doi.org/10.1016/j.procs.2015.12.283
Osman, J. (1990). Modeling and control of robot manipulators. Dissertation, City University London.https://openaccess.city.ac.uk/id/eprint/7758/1/Decentralized_and_hierarchical_control_of_robot_manipulators.pdf
Adeola-Bello, Z., & Azlan, N. (2022). Power Assist Rehabilitation Robot and Motion Intention Estimation. International Journal of Robotics and Control Systems, 2(2), 297-316. DOI: https://doi.org/10.31763/ijrcs.v2i2.650
Song, P., Yu, Y., and Zhang, X. (2019). A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation. Robotica, 37(5), 801-836. DOI: https://doi.org/10.1017/S0263574718001339
Xing, L., Wang, X., and Wang, J. (2017). A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality. International Journal of Advanced Robotic Systems, 14(6), 1–8. DOI: https://doi.org/10.1177/1729881417743283
Topini, A., Sansom, W., Secciani, N., Bartalucci, L., Ridolfi, A., andAllotta, B. (2022). Variable Admittance Control of a Hand Exoskeleton for Virtual Reality-Based Rehabilitation Tasks. Frontiers in neurorobotics, 15, 789743. DOI: https://doi.org/10.3389/fnbot.2021.789743
Shanta, M. N. T., and Azlan, N. Z. (2016). Adaptive sliding mode control with radial basis function neural network for time dependent disturbances and uncertainties. ARPN Journal of Engineering and Applied Sciences, 11(6), 4123–4129.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 IIUM Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.