Fast ℒaasso- 𝓜pc Using Extended Kalman Filter for Robot-Assisted Rehabilitation: with Optimal Impedance

Document Type : Research Article

Authors

Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Elderly people may lose the ability to walk normally as their muscles weaken, or it may be difficult for them to maintain balance while walking. In addition to aging, nerve damage such as stroke, trauma, infectious diseases, accidents, etc. may cause loss of balance in walking and weakening of muscles. Wearable robots are crucial for helping patients with lower limb diseases, particularly those with trouble walking since their numbers are rising. These robots assist patients in walking, provide comfort, and aid in recuperation. In this study, the model predictive control based on the Lasso regression theory (Lasso-MPC) and the extended Kalman filter (EKF) was used to make a novel controller that helps the patient walk by adjusting the impedance so that, in addition to regular walking, the patient has to put out the most effort when walking. In order to evaluate and the effectiveness of the proposed method, first, using \textit{OpenSIM} software, the required torque data was determined for healthy, disabled and sick people. Then, using these obtained data, the model fitting parameters were determined. At the end, experiments including a healthy person and a modular lower limb exoskeleton were performed. The findings show that the proposed method successfully estimates the patient's torque and correctly adjusts the robot's assistance level to the user's behavior, thereby maximizing his activity during treatment.

Keywords

Main Subjects

References

  1. Yuan, E. Klavon, Z. Liu, R.P. Lopez, X. Zhao, A systematic review of robotic rehabilitation for cognitive training, Frontiers in Robotics and AI, 8 (2021) 605715.
  2. -Y. Kim, S.-H. Kim, H. Ko, Design and implementation of BCI-based intelligent upper limb rehabilitation robot system, ACM Transactions on Internet Technology, 21(3) (2021) 1-17.
  3. Ahmadian, I. Sharifi, H.A. Talebi, Robust Distributed Lasso-Model Predictive Control Design: A Case Study on Large-Scale Multi-Robot Systems, AUT Journal of Modeling and Simulation, 55(1) (2023) 8-8.
  4. Ahmadian, H.A. Talebi, I. Sharifi,  1– B Adaptive Controller Design for Wrist Rehabilitation Robot, in: 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), IEEE, 2021, pp. 177-184.
  5. Bauer, Y.-J. Pan, H. Shen, Adaptive impedance control in bilateral telerehabilitation with robotic exoskeletons, in: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2020, pp. 719-725.
  6. A. Gull, S. Bai, T. Bak, A review on design of upper limb exoskeletons, Robotics, 9(1) (2020) 16.
  7. Durrer, P. Agrawal, A. Ozgul, S.C. Neuhauss, N. Nama, D. Ahmed, A robot-assisted acoustofluidic end effector, Nature Communications, 13(1) (2022) 6370.
  8. Gugliandolo, G. Campobello, P.P. Capra, S. Marino, A. Bramanti, G. Di Lorenzo, N. Donato, A movementtremors recorder for patients of neurodegenerative diseases, IEEE Transactions on Instrumentation and Measurement, 68(5) (2019) 1451-1457.
  9. Wu, X. Wang, B. Chen, H. Wu, Patient-active control of a powered exoskeleton targeting upper limb rehabilitation training, Frontiers in Neurology, 9 (2018) 817.
  10. Kalani, S.M. Tahamipour-Z, I. Kardan, A. Akbarzadeh, Assistive Control of a Hip Exoskeleton Robot, using a DQN-Adjusted Delayed Output Feedback Method, AUT Journal of Electrical Engineering, 55(1) (2023) 99-106.
  11. Brahmi, M. Driscoll, I.K. El Bojairami, M. Saad, A. Brahmi, Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer, ISA transactions, 108 (2021) 381-392.
  12. Zhang, G. Cao, W. Li, J. Chen, L. Li, D. Diao, A selfadaptive-coefficient-double-power sliding mode control method for lower limb rehabilitation exoskeleton robot, Applied Sciences, 11(21) (2021) 10329.
  13. A. Alawad, A.J. Humaidi, A.S. Alaraji, Observer sliding mode control design for lower exoskeleton system: Rehabilitation case, Journal of Robotics and Control (JRC), 3(4) (2022) 476-482.
  14. Ahmadian, H.A. Talebi, I. Sharifi, $\mathscr {L} _ {1} $ Adaptive Control Design Using CMPC: Applied to Single-Link Flexible Joint Manipulator, in: 2021 29th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2021, pp. 709-714.
  15. Wang, O.R. Barry, Inverse optimal robust adaptive controller for upper limb rehabilitation exoskeletons with inertia and load uncertainties, IEEE Robotics and Automation Letters, 6(2) (2021) 2171-2178.
  16. Sharma, P. Gaur, S. Bhatt, D. Joshi, Optimal fuzzy logic-based control strategy for lower limb rehabilitation exoskeleton, Applied Soft Computing, 105 (2021) 107226.
  17. Sheybanifar, S.M. Barakati, Simplified Model Predictive for Controlling Circulating and Output Currents of a Modular Multilevel Converter, AUT Journal of Electrical Engineering, 54(1) (2022) 121-136.
  18. Lee, I. Kim, S.-H. Lee, Estimation of the continuous walking angle of knee and ankle (talocrural joint, subtalar joint) of a lower-limb exoskeleton robot using a neural network, Sensors, 21(8) (2021) 2807.
  19. Ahmadian, M. Lotfi, M.B. Menhaj, H.A. Talebi, I. Sharifi, A novel L1 adaptive-hybrid control with guaranteed stability for a class of uncertain nonlinear systems: A case study on SA330 Puma, Journal of the Franklin Institute, 359(17) (2022) 9860-9885.
  20. Nasiri, M. Shushtari, A. Arami, An adaptive assistance controller to optimize the exoskeleton contribution in rehabilitation, Robotics, 10(3) (2021) 95.
  21. Ahmadian, H.A. Talebi, I. Sharifi, 1 Adaptive Control Design for SRS Robot Using Gaussian Process Regression, in: 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA), IEEE, 2021, pp. 1-6.
  22. M. dos Santos, A.A. Siqueira, Optimal impedance via model predictive control for robot-aided rehabilitation, Control Engineering Practice, 93 (2019) 104177.
  23. C. Jaimes, A.A. Siqueira, Preliminary evaluation of disturbance torque estimation approaches for lower-limb robotic rehabilitation, in: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), IEEE, 2019, pp. 715-720.
  24. Gallieri, M. Gallieri, Principles of LASSO MPC, Lasso-MPC–Predictive Control with ℓ1-Regularised Least Squares, (2016) 47-63.
  25. Etienne, K. Langueh, L. Rajaoarisoa, Self-triggered controller co-design using LASSO regression, in: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, pp. 139-144.
  26. F. De Gaitani, W. dos Santos, A.A.G. Siqueira, Design and performance analysis of a compact series elastic  actuator for exoskeletons, Journal of Control, Automation and Electrical Systems, 33(3) (2022) 1012-1021.
  27. Hu, J. Meng, G. Li, D. Zhao, G. Feng, G. Zuo, Y. Liu, J. Zhang, C. Shi, Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot, Sensors, 23(8) (2023) 4042.
  28. Ai, V. Santamaria, I. Omofuma, S.K. Agrawal, Effects of Boundary-Based Assist-As-Needed Force Field on Lower Limb Muscle Synergies during Standing Posture Training, IEEE Transactions on Neural Systems and Rehabilitation Engineering, (2023).
  29. M. Lopes, J. Figueiredo, C. Pinheiro, L.P. Reis, C.P. Santos, Biomechanical assessment of adapting trajectory and human-robot interaction stiffness in impedancecontrolled ankle orthosis, Journal of Intelligent & Robotic Systems, 102(4) (2021) 76.
  30. Song, Y. Yu, X. Zhang, A tutorial survey and comparison of impedance control on robotic manipulation, Robotica, 37(5) (2019) 801-836.
  31. I. Krebs, J.J. Palazzolo, L. Dipietro, M. Ferraro, J. Krol, K. Rannekleiv, B.T. Volpe, N. Hogan, Rehabilitation robotics: Performance-based progressive robot-assisted therapy, Autonomous robots, 15 (2003) 7-20.
  32. I. Krebs, B.T. Volpe, D. Williams, J. Celestino, S.K. Charles, D. Lynch, N. Hogan, Robot-aided neurorehabilitation: a robot for wrist rehabilitation, IEEE transactions on neural systems and rehabilitation engineering, 15(3) (2007) 327-335.
  33. Wu, B. Chen, H. Wu, Adaptive admittance control of an upper extremity rehabilitation robot with neuralnetwork-based disturbance observer, IEEE Access, 7 (2019) 123807-123819.
  34. J. Asl, T. Narikiyo, M. Kawanishi, An assist-asneeded control scheme for robot-assisted rehabilitation, in: 2017 American control conference (ACC), IEEE, 2017, pp. 198-203.
  35. Cui, W. Chen, X. Jin, S.K. Agrawal, Design of a 7-DOF cable-driven arm exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance, IEEE/ASME Transactions on Mechatronics, 22(1) (2016) 161-172.
  36. J. Asl, M. Yamashita, T. Narikiyo, M. Kawanishi, Field-based assist-as-needed control schemes for rehabilitation robots, IEEE/ASME Transactions on Mechatronics, 25(4) (2020) 2100-2111.
  37. Casadio, V. Sanguineti, Learning, retention, and slacking: a model of the dynamics of recovery in robot therapy, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3) (2012) 286-296.
  38. Kalani, S.M. Tahamipour-Z, I. Kardan, A. Akbarzadeh, Assistive Control of a Hip Exoskeleton Robot, using a DQN-Adjusted Delayed Output Feedback Method, AUT Journal of Electrical Engineering, 55(1) (2023) 99-106.
  39. Naghavi, A. Akbarzadeh, S.M. Tahamipour-Z, I. Kardan, Assist-As-Needed control of a hip exoskeleton based on a novel strength index, Robotics and Autonomous Systems, 134 (2020) 103667.
  40. Jarrassé, T. Charalambous, E. Burdet, A framework to describe, analyze and generate interactive motor behaviors, PloS one, 7(11) (2012) e49945.
  41. Jammeli, A. Chemori, H. Moon, S. Elloumi, S. Mohammed, An assistive explicit model predictive control framework for a knee rehabilitation exoskeleton, IEEE/ASME Transactions on Mechatronics, 27(5) (2021) 3636-3647.
  42. Nejabat, A. Nikoofard, Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems, AUT Journal of Electrical Engineering, 54(2) (2022) 209-224.
  43. Cao, J. Huang, Neural-network-based nonlinear model predictive tracking control of a pneumatic muscle actuator-driven exoskeleton, IEEE/CAA Journal of Automatica Sinica, 7(6) (2020) 1478-1488.
  44. Sheybanifar, S.M. Barakati, Simplified Model Predictive for Controlling Circulating and Output Currents of a Modular Multilevel Converter, AUT Journal of Electrical Engineering, 54(1) (2022) 121-136.
  45. Saleh, A. Deihimi, Model Predictive Control of Distributed Energy Resources with Predictive Set-Points for Grid-Connected Operation, AUT Journal of Electrical Engineering, 50(2) (2018) 109-120.
  46. Li, Y. Shi, L. Hu, Z. Sun, A generalized model predictive control method for series elastic actuator driven exoskeleton robots, Computers & Electrical Engineering, 94 (2021) 107328.
  47. Katayama, M. Murooka, Y. Tazaki, Model predictive control of legged and humanoid robots: models and algorithms, Advanced Robotics, 37(5) (2023) 298-315.
  48. Hu, H. Su, L. Zhang, S. Miao, G. Chen, A. Knoll, Nonlinear model predictive control for mobile robot using varying-parameter convergent differential neural network, Robotics, 8(3) (2019) 64.
  49. Dunkelberger, S.A. Carlson, J. Berning, K.C. Stovicek, E.M. Schearer, M.K. O’Malley, Shared control of elbow movements with functional electrical stimulation and exoskeleton assistance, in: 2022 International Conference on Rehabilitation Robotics (ICORR), IEEE, 2022, pp. 1-6.
  50. J. Prado, M. Torres-Torriti, F.A. Cheein, Distributed tube-based nonlinear MPC for motion control of skidsteer robots with terra-mechanical constraints, IEEE Robotics and Automation Letters, 6(4) (2021) 80458052.
  51. Tallamraju, S. Rajappa, M.J. Black, K. Karlapalem, Ahmad, Decentralized mpc based obstacle avoidance for multi-robot target tracking scenarios, in: 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, 2018, pp. 1-8. 
  52. Salzmann, E. Kaufmann, J. Arrizabalaga, M. Pavone, D. Scaramuzza, M. Ryll, Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms, IEEE Robotics and Automation Letters, 8(4) (2023) 2397-2404.
  53. J. Ostafew, A.P. Schoellig, T.D. Barfoot, Robust constrained learning-based NMPC enabling reliable mobile robot path tracking, The International Journal of Robotics Research, 35(13) (2016) 1547-1563.
  54. Scianca, D. De Simone, L. Lanari, G. Oriolo, MPC for humanoid gait generation: Stability and feasibility, IEEE Transactions on Robotics, 36(4) (2020) 1171-1188.
  55. Caulcrick, W. Huo, E. Franco, S. Mohammed, W. Hoult, R. Vaidyanathan, Model predictive control for human-centred lower limb robotic assistance, IEEE Transactions on Medical Robotics and Bionics, 3(4) (2021) 980-991.
  56. M. Gallieri, Lasso-MPC–Predictive Control with l1Regularised Least Squares, Springer, 2016

Files

Share

How to cite

Statistics