Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T04:54:32.207Z Has data issue: false hasContentIssue false
  • English
  • Français

RBF neural network-based admittance PD control for knee rehabilitation robot

Published online by Cambridge University Press:  03 August 2022

Karam Almaghout ,
Bahram Tarvirdizadeh Open the ORCID record for Bahram Tarvirdizadeh [Opens in a new window] ,
Khalil Alipour Open the ORCID record for Khalil Alipour [Opens in a new window]  and
Alireza Hadi Open the ORCID record for Alireza Hadi [Opens in a new window]
Karam Almaghout
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran Institute of Robotics and Computer Vision, Innopolis University, Innopolis, Russia
Bahram Tarvirdizadeh*
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Khalil Alipour
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Alireza Hadi
Affiliation:
Advanced Service Robots (ASR) Lab., Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.

Keywords

rehabilitation robotics systemsliding mode controlradial basis functioninteraction torquesmotion recovery
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4512 - 4534
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Wang, W., Hou, Z.-G., Tong, L., Zhang, F., Chen, Y. and Tan, M. , “A novel leg orthosis for lower limb rehabilitation robots of the sitting/lying type,” Mech. Mach. Theory 74(5), 337–353 (2014).CrossRefGoogle Scholar
Farjadian, A. B., Nabian, M., Mavroidis, C. and Holden, M. K., “Implementation of A Task-Dependent Anisotropic Impedance Controller Into A 2-DOF Platform-Based Ankle Rehabilitation Robot,” In: IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015).Google Scholar
Wang, H., Feng, Y., Zhang, D., Chen, F., Du, Y., Yan, H. and Vladareanu, L., “Retracted: Research on a new applicable lower limb rehabilitation robot,” J. Fundamental Appl. Sci. 10(4S), 181–186 (2018).Google Scholar
Hussain, S., Xie, S. Q. and Jamwal, P. K., “Control of a robotic orthosis for gait rehabilitation,” Robot. Autonom. Syst. 61(9), 911–919 (2013).CrossRefGoogle Scholar
Naghavi, N. and Mahjoob, M. J., “Design and control of an active 1-DoF mechanism for knee rehabilitation,” Disab. Rehabil. Assist. Technol. 11(7), 588–594 (2016).CrossRefGoogle ScholarPubMed
Paz, P., Oliveira, T. Q., Pino, A. V. and Fontana, A. P., “Model-Free neuromuscular electrical stimulation by stochastic extremum seeking,” IEEE Trans Contr. Syst. Technol. 28, 238–253 (2019).CrossRefGoogle Scholar
Roux-Oliveira, T., Costa, L. R., Pino, A. V. and Paz, P., “Extremum seeking-based adaptive PID control applied to neuromuscular electrical stimulation,” Anais da Academia Brasileira de Ciências 91(suppl. 1), e20180544 (2019).CrossRefGoogle ScholarPubMed
Han, S., Wang, H. and Tian, Y., “A linear discrete-time extended state observer-based intelligent PD controller for a 12 DOFs lower limb exoskeleton LLE-RePA,” Mech. Syst. Signal Process. 138(3), 106547 (2020).CrossRefGoogle Scholar
Yousefi, F., Alipour, K., Tarvirdizadeh, B. and Hadi, A., “Knee Rehabilitation Robot Control By Sliding-Backstepping and Admittance Control,” In: Artificial Intelligence and Robotics (IRANOPEN) (IEEE, 2017).Google Scholar
Riani, A., Madani, T., Benallegue, A. and Djouani, K.,“Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton,” Control Eng. Pract. 75(2), 108–117 (2018).Google Scholar
Oliveira, T. R., Costa, L. R., Catunda, J. M. Y., Pino, A. V., Barbosa, W. and de Souza, M. N., “Time-scaling based sliding mode control for neuromuscular electrical stimulation under uncertain relative degrees,” Med. Eng. Phys. 44(9), 53–62 (2017).CrossRefGoogle ScholarPubMed
Li, X., Li, F., Zhang, X., Yang, C. and Gui, W., “Exponential stability analysis for delayed semi-Markovian recurrent neural networks: A homogeneous polynomial approach,” IEEE Trans. Neur. Net. Learn. 29(12), 6374–6384 (2018).CrossRefGoogle ScholarPubMed
Beyhan, S. and Alcı, M., “Stable modeling based control methods using a new RBF network,” ISA Trans. 49(4), 510–518 (2010).CrossRefGoogle Scholar
Poultangari, I., Shahnazi, R. and Sheikhan, M., “RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm,” ISA Trans. 51(5), 641–648 (2012).CrossRefGoogle ScholarPubMed
Zhang, P., Zhang, J. and Zhang, Z., “Design of RBFNN-based adaptive sliding mode control strategy for active rehabilitation robot,” IEEE Access 8, 155538–155547 (2020).CrossRefGoogle Scholar
Shi, J., Xu, L., Cheng, G., Xu, J., Chen, S. and Liang, X., “Trajectory Tracking Control Based on RBF Neural Network of The Lower Limb Rehabilitation Robot,” In: 2020 IEEE International Conference on Mechatronics and Automation (ICMA) (IEEE, 2020).Google Scholar
Akiyama, Y., Yamada, Y., Ito, K., Oda, S., Okamoto, S. and Hara, S., “Test Method for Contact Safety Assessment of A Wearable Robot-Analysis of Load Caused By A Misalignment of the Knee Joint,” In: 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication (IEEE, 2012).CrossRefGoogle Scholar
He, Y., Eguren, D., Luu, T. P. and Contreras-Vidal, J. L., “Risk management and regulations for lower limb medical exoskeletons: A review,” Medical Dev. 10, 89 (2017).Google ScholarPubMed
Mallat, R., Khalil, M., Venture, G., Bonnet, V. and Mohammed, S., “Human-exoskeleton Joint Misalignment: A Systematic Review,” In: Fifth International Conference on Advances in Biomedical Engineering (ICABME) (IEEE, 2019).Google Scholar
Bansil, S., Prakash, N., Kaye, J., Wrigley, S., Manata, C., Stevens-Haas, C. and Kurlan, R., “Movement disorders after stroke in adults: A review,” Tremor Other Hyperkinetic Movements 2, tre-02-42-195-1 (2012).CrossRefGoogle ScholarPubMed
Alarcón, F., Zijlmans, J. C. M., Dueñas, G. and Cevallos, N.,  “Post-stroke movement disorders: report of 56 patients,” J. Neurol. Neurosurg. Psych. 75(11), 1568–1574 (2004).CrossRefGoogle ScholarPubMed
Bessler, J., Prange-Lasonder, G. B., Schaake, L, Saenz, J. F., Bidard, C., Fassi, I., Valori, M., Lassen, A. B. and Buurke, J. H., “Safety assessment of rehabilitation robots: a review identifying safety skills and current knowledge gaps,” Front. Robot. AI 8, 33 (2021).Google ScholarPubMed
Sharkawy, A.-N., Koustoumpardis, P. N. and Aspragathos, N., “A recurrent neural network for variable admittance control in human-robot cooperation: simultaneously and online adjustment of the virtual damping and inertia parameters,” Int. J. Intell. Robot. Appl. 4(4), 441–464 (2020).CrossRefGoogle Scholar
Yu, W. and Perrusquía, A., “Simplified stable admittance control using end-effector orientations,” Int. J. Soc. Robot. 12(5), 1061–1073 (2020).CrossRefGoogle Scholar
Ochoa Luna, C., Rahman, M. H., Saad, M., Archambault, P. S. and Ferrer, S. B., “Admittance-based upper limb robotic active and active-assistive movements,” Int. J. Adv. Robot. Syst. 12(9), 117 (2015).CrossRefGoogle Scholar
Taherifar, A., Vossoughi, G. and Ghafari, A. S., “Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric,” Robotica 36(3), 427–447 (2018).Google Scholar
Gupta, S. and Sharma, S., “Selection and application of advance control systems: PLC, DCS and PC-based system.” J. Sci. Ind. Res. 64, 249–255 (2005).Google Scholar
Akdoğan, E., Taçgın, E. and Adli, M. A., “Knee rehabilitation using an intelligent robotic system,” J. Intell. Manuf. 20(2), 195–202 (2009).CrossRefGoogle Scholar
Morin, D.. The Lagrangian Method, in Introduction to Classical Mechanics: with Problems and Solutions (Cambridge University Press, 2008).Google Scholar
Song, P., Yu, Y. and Zhang, X., “A tutorial survey and comparison of impedance control on robotic manipulation,” Robotica 37(5), 801–836 (2019).CrossRefGoogle Scholar
Feng, H., Yin, C., Weng, W., Ma, W., Zhou, J., Jia, W. and Zhang, Z. , “Robotic excavator trajectory control using an improved GA based PID controller,” Mech. Syst. Signal Process. 105, 153–168 (2018).CrossRefGoogle Scholar
Shtessel, Y., Edwards, C., Fridman, L. and Levant, A., Sliding Mode Control and Observation, vol. 10. (Birkhäuser New York, New York, 2014).CrossRefGoogle Scholar
Roy, S., Baldi, S. and Fridman, L. M., “On adaptive sliding mode control without a priori bounded uncertainty,” Automatica 111(12), 108650 (2020).CrossRefGoogle Scholar
Babaiasl, M., et al., “Sliding Mode Control of an Exoskeleton Robot for use in Upper-Limb Rehabilitation,” In: 3rd RSI International Conference on Robotics and Mechatronics (ICROM) (IEEE, 2015).CrossRefGoogle Scholar
Hussain, S., Xie, S. Q. and Jamwal, P. K., “Adaptive impedance control of a robotic orthosis for gait rehabilitation,” IEEE Trans. Cybern. 43(3), 1025–1034 (2013).CrossRefGoogle ScholarPubMed
Torabi, M., Sharifi, M. and Vossoughi, G., “Robust adaptive sliding mode admittance control of exoskeleton rehabilitation robots,” Sci. Iran 25(5), 2628–2642 (2018).Google Scholar
Eker, I., “Sliding mode control with PID sliding surface and experimental application to an electromechanical plant,” ISA Trans. 45(1), 109–118 (2006).CrossRefGoogle Scholar
Kara, T. and Mary, A. H., “Adaptive PD-SMC for nonlinear robotic manipulator tracking control,” Stud. Inform. Control 26(1), 49–58 (2017).CrossRefGoogle Scholar
Sharkawy, A.-N. and Koustoumpardis, P. N., “Dynamics and computed-torque control of a 2-DOF manipulator: Mathematical analysis,” Int. J. Adv. Sci. Technol. 28(12), 201–212 (2019).Google Scholar
Nguyen, V.-T., Lin, C.-Y., Su, S.-F., and Tran, Q.-V., “Adaptive chattering free neural network based sliding mode control for trajectory tracking of redundant parallel manipulators,” Asian J. Control 21(2), 908–923 (2018).Google Scholar
Li, J. and Tian, H., “Position control of SMA actuator based on inverse empirical model and SMC-RBF compensation,” Mech. Syst. Signal Process. 108, 203–215 (2018).CrossRefGoogle Scholar
Ljung, L., System Identification—Theory for the User, 2nd edn. (Upper Saddle River, NJ: PTR Prentice Hall). Feasible Mind Uploading, vol. 101 (1999).Google Scholar
Nelles, O., Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models (Springer Berlin, Heidelberg, 2001).CrossRefGoogle Scholar