Hostname: page-component-54dcc4c588-mz6gc Total loading time: 0 Render date: 2025-10-13T11:04:24.434Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust control based on Lyapunov stability theory for the joint modules in hip-assist exoskeleton robots

Published online by Cambridge University Press:  13 October 2025

Xiaoli Liu Open the ORCID record for Xiaoli Liu [Opens in a new window] ,
Youli Hu ,
Faliang Wang Open the ORCID record for Faliang Wang [Opens in a new window] ,
Shengchao Zhen Open the ORCID record for Shengchao Zhen [Opens in a new window]  and
Ye-Hwa Chen
Xiaoli Liu
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, Anhui, PR China Human-Computer Collaborative Robot Joint Laboratory of Anhui Province, Hefei, Anhui, PR China
Youli Hu
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, Anhui, PR China
Faliang Wang*
Affiliation:
School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, PR China
Shengchao Zhen
Affiliation:
Human-Computer Collaborative Robot Joint Laboratory of Anhui Province, Hefei, Anhui, PR China School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, PR China
Ye-Hwa Chen
Affiliation:
The Geroge W.Woodruf School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
*
Corresponding author: Faliang Wang; Email: faliang941118@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a novel robust control method for a hip-assist exoskeleton robot’s joint module, addressing dynamic performance under variable loads. The proposed approach integrates traditional PID control with robust, model-based strategies, utilizing the system’s dynamic model and a Lyapunov-based robust controller to handle uncertainties. This method not only enhances traditional PID control but also offers practical advantages in implementation. Theoretical analysis confirms the system’s uniform boundedness and ultimate boundedness. A Matlab prototype was developed for simulation, demonstrating the control scheme’s feasibility and effectiveness. Numerical simulations show that the proposed fractional-order hybrid PD (FHPD) controller significantly reduces tracking error by 58.70% compared to the traditional PID controller, 55.41% compared to the MPD controller, and 32.32% compared to ADRC, highlighting its superior tracking performance and stability.

Keywords

hip-assist exoskeleton robotrobust controljoint moduledisc motorLyapunov method

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 17
Copyright
© The Author(s), 2025. 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.)

Article purchase

Temporarily unavailable

References

Xue, T., Wang, Z., Zhang, T., Zhang, M. and Li, Z., “The Control System for Flexible Hip Assistive Exoskeleton,” In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2018) pp. 697–702.Google Scholar
Xue, T., Wang, Z., Zhang, T., Zhang, M. and Li, Z., “Adaptive oscillator-based robust control for flexible hip assistive exoskeleton,” IEEE Rob. Autom. Lett. 4(4), 3318–3323 (2019).10.1109/LRA.2019.2926678CrossRefGoogle Scholar
Wu, Q., Wang, X., Du, F. and Zhang, X., “Design and control of a powered hip exoskeleton for walking assistance,” Int. J. Adv. Rob. Syst. 12(3), 18 (2015).10.5772/59757CrossRefGoogle Scholar
Wei, W., Zha, S., Xia, Y., Gu, J. and Lin, X., “A hip active assisted exoskeleton that assists the semi-squat lifting,” Appl. Sci. 10(7), 2424 (2000).10.3390/app10072424CrossRefGoogle Scholar
Zhang, T., Tran, M. and Huang, H., “Design and experimental verification of hip exoskeleton with balance capacities for walking assistance,” IEEE/ASME Trans. Mechatron. 23(1), 274–285 (2018).10.1109/TMECH.2018.2790358CrossRefGoogle Scholar
Zhang, Q., Nalam, V., Tu, X., Li, M., Si, J., Lewek, M. D. and Huang, H. H., “Imposing healthy hip motion pattern and range by exoskeleton control for individualized assistance,” IEEE Rob. Autom. Lett. 7(4), 11126–11133 (2022).10.1109/LRA.2022.3196105CrossRefGoogle Scholar
Zhen, S., Zhao, Z., Liu, X., Chen, F., Zhao, H. and Chen, Y. H., “A novel practical robust control inheriting pid for scara robot,” IEEE Access. 8, 227409–227419 (2020).10.1109/ACCESS.2020.3045789CrossRefGoogle Scholar
Ha, T.-J., Lee, J. and Park, J. H., “Robust Control by Inverse Optimal PID Approach for Flexible Joint Robot Manipulator,” In: 2007 IEEE International 410 Conference on Robotics and Biomimetics (ROBIO) (2007) pp. 336–341.Google Scholar
Khaled, T. A., Akhrif, O. and Bonev, I. A., “Dynamic path correction of an industrial robot using a distance sensor and an ADRC controller,” IEEE/ASME Trans. Mechatron. 26(3), 1646–1656 (2020).10.1109/TMECH.2020.3026994CrossRefGoogle Scholar
Zhong, S., Huang, Y. and Guo, L., “An ADRC-based pid tuning rule,” Int. J. Robust Nonlinear Control. 32(18), 9542–9555 (2022).10.1002/rnc.5845CrossRefGoogle Scholar
Li, X., Ai, W., Gao, Z. and Tian, S., “Robust Adrc for Nonlinear Time-Varying System with Uncertainties,” In: 2017 6th Data Driven Control and Learning Systems (DDCLS) (IEEE) (2017) pp. 350–356.10.1109/DDCLS.2017.8068096CrossRefGoogle Scholar
Rsetam, K., Cao, Z. and Man, Z., “Design of robust terminal sliding mode control for underactuated flexible joint robot,” IEEE Trans. Syst. Man Cybern. Syst. 52(7), 4272–4285 (2022).10.1109/TSMC.2021.3096835CrossRefGoogle Scholar
Aswani, A., Gonzalez, H., Sastry, S. S. and Tomlin, C., “Provably safe and robust learning-based model predictive control,” Automatica. 49(5), 1216–1226 (2013).10.1016/j.automatica.2013.02.003CrossRefGoogle Scholar
Zeng, T., Mohammad, A., Madrigal, A. G., Axinte, D. and Keedwell, M., “A robust human–robot collaborative control approach based on model predictive control,” IEEE Trans. Ind. Electron. 71(7), 7360–7369 (2023).10.1109/TIE.2023.3299046CrossRefGoogle Scholar
Wang, D., Wei, W., Yeboah, Y., Li, Y. and Gao, Y., “A robust model predictive control strategy for trajectory tracking of omni-directional mobile robots,” J. Intell. Rob. Syst. 98(2), 439–453 (2020).10.1007/s10846-019-01083-1CrossRefGoogle Scholar
Zhao, R., Wu, L. and Chen, Y.-H., “Robust control for nonlinear delta parallel robot with uncertainty: An online estimation approach,” IEEE Access. 8, 97604–97617 (2020).10.1109/ACCESS.2020.2997093CrossRefGoogle Scholar
Hwang, S., Park, S. H., Jin, M. and Kang, S. H., “A robust control of robot manipulators for physical interaction: Stability analysis for the interaction with unknown environments,” Intell. Serv. Rob. 14(3), 471–484 (2021).10.1007/s11370-021-00370-xCrossRefGoogle Scholar
Qin, F., Zhao, H., Zhen, S., Sun, H. and Zhang, Y., “Lyapunov based robust control for tracking control of lower limb rehabilitation robot with uncertainty,” Autom. Syst. 18, 76–84 (2020).10.1007/s12555-019-0175-5CrossRefGoogle Scholar
Dawson, C., Qin, Z., Gao, S. and Fan, C., “Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions,” In: 2022 Conference on Robot Learning. PMLR. (2022) pp. 1724–1735.Google Scholar
Yao, B. and Jiang, C., “Advanced Motion Control: From Classical PID to Nonlinear Adaptive Robust Control,” In: 2010 11th IEEE International Workshop On Advanced Motion Control (AMC) (IEEE) (2010) pp. 815–829.Google Scholar
Moreno-Valenzuela, J., Quevedo-Pillado, Y., Pérez-Aboytes, R. and González-Hernández, L., “Lyapunov-based adaptive control for the permanent magnet synchronous motor driving a robotic load,” J. Circuits Syst. Comput. 26(11), 1750168 (2017).10.1142/S0218126617501687CrossRefGoogle Scholar
Marques, F., Flores, P., Claro, J. P. and Lankarani, H. M., “A survey and comparison of several friction force models for dynamic analysis of multibody mechanical systems,” Nonlinear Dyn. 86, 1407–1443 (2016).10.1007/s11071-016-2999-3CrossRefGoogle Scholar
Pennestr‘ı, E., Rossi, V., Salvini, P. and Valentini, P. P., “Design and experimental verification of hip exoskeleton with balance capacities for walking assistance,” Nonlinear Dyn. 83, 1785–1801 (2016).Google Scholar
Shao, X., Liu, N., Wang, Z., Zhang, W. and Yang, W., “Neuroadaptive integral robust control of visual quadrotor for tracking a moving object,” Mech. Syst. Signal Process. 136, 106513 (2020).10.1016/j.ymssp.2019.106513CrossRefGoogle Scholar
Tang, Y. W., Sun, H. X., Li, X. and Song, J. Z., “Nonlinear Friction Modeling for Modular Robot Joints,” In: MECHANICS AND MECHANICAL ENGINEERING: Proceedings of the 2015 International Conference (MME2015) (2016) pp. 908–915.Google Scholar
Slotine, J.-J. E. and Li, W., “Applied nonlinear control,” Appl. Nonlinear Control. 199(705) (1991).Google Scholar
Corless, M. and Leitmann, G., “Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems,” IEEE Trans. Autom. Control. 26(5), 1139–1144 (1981).10.1109/TAC.1981.1102785CrossRefGoogle Scholar
Bechlioulis, C. P. and Rovithakis, G. A., “Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems,” Automatica. 43(2), 31 (2009).Google Scholar