CONTROL STRATEGY FOR POWER ASSIST UPPER LIMB REHABILITATION ROBOT WITH THE THERAPIST’S MOTION INTENTION PREDICTION
Keywords:
test1, test 2Abstract
Currently, fully automated rehabilitation robots can assist therapists in providing rehabilitation therapy. However, since the robot is totally automated, 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 and 0.005rad/sec for sinusoidal and constant torque values, respectively.
ABSTRAK: Pada masa ini, robot rehabilitasi automatik sepenuhnya boleh membantu ahli terapi dalam menyediakan terapi pemulihan. Walau bagaimanapun, disebabkan robot tercebat sepenuhnya automatik, pesakit boleh cedera. Sebaliknya, rawatan manual mungkin menyebabkan kurang kecederaan pesakit tetapi ia memenatkan, dan tiada ahli terapi yang mencukupi di kebanyakan negara. Robot pemulihan bantuan kuasa dapat menyokong ahli terapi dan boleh membantu untuk mengurangkan masalah ini. Sistem ini menggabungkan kelebihan terapi pemulihan robotik dan manual. Matlamat kajian ini adalah untuk membangunkan strategi kawalan untuk robot untuk membantu pergerakan ahli terapi dalam rawatan pemulihan anggota atas (tangan). Sistem ini menggabungkan kelebihan retapi pemulihan robotik dan manual. Alat pengukar tork dan kedudukan yang dipasang pada lengan robot pemulihan anggota atas digunakan untuk anggaran niat gerakan abli terapi. Jumlah halaju sudut yang perlu dihantar kepada pengawal maklum balas akan ditentukan dengan meramalkan niat gerakan ahli terapi menggunakan kaedah kawalan interaksi impedans. Halaju yang terhasil daripada penganggar niat gerakan dimasukkan ke dalam pengawal adaptif berasaskan Kawalan Mod Gelongsor - Teknik Penghampiran 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 pergerakan ahli terapi. Pengawal yang dicadangkan digunakan untuk mengawal lenturan siku dan gerakan lanjutan pada robot pemulihan dengan satu darjah kebebasan (DOF). Sistem kawalan yang dicadangkan telah diuji menggunakan simulasi MATLAB dan ujian eksperimen perkakasan. Hasilnya menunjukkan keberkesanan pengawal yang dicadangkan dalam mengarahkan robot pemulihan mengikut trajektori yang dikehendaki berdasarkan niat gerakan ahli terapi, dengan ralat maksimum masing-masing 0.002rad/s dan 0.005rad/s untuk nilai tork sinusoidal dan malar.
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.
Zhang, K., Chen, X., Liu, F., Tang, H., Wang, J., and Wen, W. (2018). System framework of robotics in upper limb rehabilitation on poststroke motor recovery. Behavioural Neurology, 2018, 1–14.
Bogue, R. (2018). Rehabilitation robots. Industrial Robot, 45(3), 301–306.
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.
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.
Mansour, M. (2021). Conceptual Design of EMG Based Upper Limb Power Assist Rehabilitation Device. Journal of Smart Systems Research (JOINSSR), 2(1), 1–17.
Kiguchi, K., Kose, Y., and Hayashi, Y. (2010). An upper-limb power-assist exoskeleton robot with task-oriented perception-assist. 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2010, 88–93.
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.
Ali, A., Ahmed, S. F., Kadir, K. A., Joyo, M. K., and Yarooq, R. N. S. (2018). Fuzzy PID controller for upper limb rehabilitation robotic system. 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018, 1–5.
Mounis, S. Y. A., Azlan, N. Z., and Fatai, S. (2019). Optimal Linear Quadratic Gaussian Torque Controller (LQG) for Upper Limb Rehabilitation. 2019 7th International Conference on Mechatronics Engineering (ICOM), 2019, 1-6.
Losey, D. P., and O’Malley, M. K. (2020). Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions. ACM Transactions on Human-Robot Interaction, 9(1), 1–19.
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.
Huang, G., Huang, G. Bin, Song, S., and You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48.
Khoshdel, V., and Akbarzadeh, A. (2018). An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics. Industrial Robot, 45(3), 416–423.
Xia, P., Hu, J., and Peng, Y. (2018). EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks. Artificial Organs, 42(5), 67–77.
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.
Brahmi, B., Laraki, M. H., Saad, M., Rahman, M. H., Ochoa-Luna, C., and Brahmi, A. (2019). Compliant adaptive control of human upper-limb exoskeleton robot with unknown dynamics based on a Modified Function Approximation Technique (MFAT). Robotics and Autonomous Systems, 117, 92–102.
Zhang, L., Liu, G., Han, B., Wang, Z., and Zhang, T. (2019). SEMG Based Human Motion Intention Recognition. Journal of Robotics, 2019, 1–12.
Kiguchi, K., and Chathuramali, M. (2019). A Study on Real-Time Detection of Interacting Motion Intention for Perception-Assist with an Upper-Limb Wearable Power-Assist Robot. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 900–905.
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.
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.
Staudenmann, D., Roeleveld, K., Stegeman, D. F., and van Dieen, J. H. (2010). Methodological aspects of SEMG recordings for force estimation - A tutorial and review. Journal of Electromyography and Kinesiology, 20(3), 375–387.
Tu, X., Huang, J., Yu, L., Xu, Q., and He, J. (2012). Design of a wearable rehabilitation robot integrated with functional electrical stimulation. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 1555–1560
Khan, A. M., Khan, F., and Han, C. (2016). Estimation of desired motion intention using extreme learning machine for upper limb assist exoskeleton. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 919–923.
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.
Kiguchi, K. (2007). A study on EMG-based human motion prediction for power assist exoskeletons. Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007, 190–195.
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.
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.
Antuvan, C. W. (2019). Decoding Human Motion Intention using Myoelectric Signals for Assistive Technologies. DR-NTU, 1–146. https://dr.ntu.edu.sg
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.
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.
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.
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.
Yang, Q., Xie, C., Tang, R., Liu, H., and Song, R. (2020). Hybrid active control with human intention detection of an upper-limb cable-driven rehabilitation robot. IEEE Access, 8, 195206–195215.
Dehghan, S. A. M., Danesh, M., and Sheikholeslam, F. (2015). Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty. Advanced Robotics, 29(4), 209–223.
Cong, S., Liang, Y., and Shang, W. (2009). Function Approximation-based Sliding Mode Adaptive Control for Time-varying Uncertain Nonlinear Systems. Frontiers in Adaptive Control.
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.
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.
Liu, G., and Fang, L. (2020). Frequency-division based hybrid force / position control of robotic arms manipulating in uncertain environments. Industrial Robot: The International Journal Ofrobotics Research and Application, 3, 445–452.
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.
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.
Song, P., Yu, Y., and Zhang, X. (2019). A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation. Robotica, 37(5), 801-836.
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.
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.
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.
