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Model identification and admittance control with neighborhood field optimization in human-exoskeleton cooperative motion

Published online by Cambridge University Press:  22 December 2025

Haoran Zhan ,
Jiange Kou ,
Yuanchao Cao ,
Wanqi Wang ,
Jiyu Zhang Open the ORCID record for Jiyu Zhang [Opens in a new window] ,
Yan Shi  and
Qing Guo Open the ORCID record for Qing Guo [Opens in a new window]
Haoran Zhan
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Jiange Kou
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China
Yuanchao Cao
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Wanqi Wang
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Jiyu Zhang
Affiliation:
Hangzhou RoboCT Technology Development Co. Ltd., Hangzhou, China
Yan Shi
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China
Qing Guo*
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
*
Corresponding Author: Qing Guo; Email: guoqinguestc@uestc.edu.cn
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Abstract

The lower limb exoskeleton is a typical wearable robot designed to assist human motion. However, its system stability and performance are often compromised due to unknown model parameters and inadequate control strategies. Therefore, it is crucial to explore the parametric identification of the exoskeleton and the design of corresponding control strategies for human-exoskeleton cooperative motion. First, an exoskeleton platform is developed to provide experimental validation. Simultaneously, a two-degree-of-freedom (2-DOF) exoskeleton model is constructed using the Lagrange method. The neighborhood field optimization (NFO) technique is then applied to identify the unknown model parameters of the exoskeleton. Additionally, the excitation trajectories for the exoskeleton are developed by the NFO method, incorporating several motion constraints to enhance the accuracy of model identification. An admittance controller is implemented to enable active control of the exoskeleton, allowing it to align with human intention and thereby improving the wearability and comfort of the device. Finally, both simulation and experimental results are compared and verified on the platform. These results demonstrate that the NFO method achieves superior identification accuracy compared to particle swarm optimization (PSO) and genetic algorithm (GA).

Keywords

exoskeletonneighborhood field optimization (NFO)model identificationadmittance control

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 16
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Bettella, F., Tortora, S., Menegatti, E., Petrone, N. and Del, A. F., “A scoping review on lower limb exoskeleton actuation’s description and characteristics,” Robotica 43(4), 1572–1589 (2025).10.1017/S0263574725000220CrossRefGoogle Scholar
Baud, R., Manzoori, A. R., Ijspeert, A. and Bouri, M., “Review of control strategies for lower-limb exoskeletons to assist gait,” J. Neuroeng. Rehabil. 18(1), 1–34 (2021).10.1186/s12984-021-00906-3CrossRefGoogle ScholarPubMed
Khamar, M. and Edrisi, M., “Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer,” Mechatronics 54, 121–132 (2018).10.1016/j.mechatronics.2018.07.010CrossRefGoogle Scholar
Aguirre-Ollinger, G., Colgate, J. E., Peshkin, M. A. and Goswami, A., “Design of an active one-degree-of-freedom lower-limb exoskeleton with inertia compensation,” Int. J. Robot. Res. 30(4), 486–499 (2011).10.1177/0278364910385730CrossRefGoogle Scholar
Tsukahara, A., Hasegawa, Y., Eguchi, K. and Sankai, Y., “Restoration of gait for spinal cord injury patients using HAL with intention estimator for preferable swing speed,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 308–318 (2015).10.1109/TNSRE.2014.2364618CrossRefGoogle ScholarPubMed
Li, Y., Guan, X., Han, X., Tang, Z., Meng, K., Shi, Z., Penzlin, B., Yang, Y., Ren, J., Yang, Z., Li, Z., Leonhardt, S. and Ji, L., “Design and preliminary validation of a lower limb exoskeleton with compact and modular actuation,” IEEE Access 8, 66338–66352 (2020).10.1109/ACCESS.2020.2985910CrossRefGoogle Scholar
Centeno-Barreda, D., Salazar-Cruz, S., Lopez-Gutierrez, R., Rosales-Luengas, Y. and Rogelio, L., “Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control,” Robotica 42(11), 3819–3838 (2024).10.1017/S0263574724001668CrossRefGoogle Scholar
Yang, Y., Ma, L. and Huang, D., “Development and repetitive learning control of lower limb exoskeleton driven by electrohydraulic actuators,” IEEE Trans. Ind. Electron. 64(5), 4169–4178 (2017).10.1109/TIE.2016.2622665CrossRefGoogle Scholar
Li, X., Liu, Y.-H. and Yu, H., “Iterative learning impedance control for rehabilitation robots driven by series elastic actuators,” Automatica 90, 1–7 (2018).10.1016/j.automatica.2017.12.031CrossRefGoogle Scholar
Centeno-Barreda, D., Salazar-Cruz, S., López-Gutiérrez, R., Rosales-Luengas, Y. and Lozano, R., “Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control,” Robotica 42(11), 3819–3838 (2024).10.1017/S0263574724001668CrossRefGoogle Scholar
Guo, Q., Zhang, Y., Celler, B. G. and Su, S. W., “Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint,” IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3572–3583 (2018).10.1109/TNNLS.2018.2854699CrossRefGoogle ScholarPubMed
Han, Y., Wu, J., Liu, C. and Xiong, Z., “Static model analysis and identification for serial articulated manipulators,” Robot. Cim-INT. Manuf. 57(5), 155–165 (2019).10.1016/j.rcim.2018.11.010CrossRefGoogle Scholar
Guo, Q., Chen, Z., Yan, Y., Xiong, W., Jiang, D. and Shi, Y., “Model identification and human–robot coupling control of lower limb exoskeleton with biogeography-based learning particle swarm optimization,” Int. J. Control Autom. Syst. 20(2), 589–600 (2022).10.1007/s12555-020-0632-1CrossRefGoogle Scholar
Swevers, J., Ganseman, C., Tukel, D., de Schutter, J. and Van Brussel, H., “Optimal robot excitation and identification,” IEEE Trans. Robot. Autom. 13(5), 730–740 (1997).10.1109/70.631234CrossRefGoogle Scholar
Young, A. J. and Ferris, D. P., “State of the art and future directions for lower limb robotic exoskeletons,” IEEE Trans. Neural Syst. Rehabil. Syst. Eng. 25(2), 171–182 (2017).10.1109/TNSRE.2016.2521160CrossRefGoogle ScholarPubMed
Cao, Q., Li, L., Li, J., Li, R. and Wang, X., “A methodology to quantify human–robot interaction forces: a case study of a 4-DOFs upper extremity rehabilitation robot,” Robotica 43(4), 1469–1490 (2025).10.1017/S0263574725000335CrossRefGoogle Scholar
Bonnet, V., Fraisse, P., Crosnier, A., Gautier, M., Gonzalez, A. and Venture, G., “Optimal exciting dance for identifying inertial parameters of an anthropomorphic structure,” IEEE Trans. Robot. 32(4), 823–836 (2016).10.1109/TRO.2016.2583062CrossRefGoogle Scholar
Jiang, Y., Wang, Y.-G., Fu, L. and Wang, X., “Robust estimation using modified hubers functions with new tails,” Technometrics 61(1), 111–122 (2019).10.1080/00401706.2018.1470037CrossRefGoogle Scholar
Yang, Y., Li, S., Li, Z., Zhou, Z. and Wang, J., “Development of a novel unpowered rigid-flexible coupling waist exoskeleton through dynamic dimensional synthesis inspired by biomimetic cooperation,” Robotica 43(6), 1–27 (2025).10.1017/S0263574725000724CrossRefGoogle Scholar
Bettella, F., Tortora, S., Menegatti, E., Petrone, N. and Felice, A. D., “A scoping review on lower limb exoskeleton actuation’s description and characteristics,” Robotica 43(4), 1–18 (2025).10.1017/S0263574725000220CrossRefGoogle Scholar
Shi, D., Zhang, W., Zhang, W. and Ding, X., “A review on lower limb rehabilitation exoskeleton robots,” Chin. J. Mech. Eng. 32(1), 1–11 (2019).10.1186/s10033-019-0389-8CrossRefGoogle Scholar
Modares, H., Ranatunga, I., Lewis, F. L. and Popa, D. O., “Optimized assistive human–robot interaction using reinforcement learning,” IEEE Trans Cybern. 46(3), 655–667 (2015).10.1109/TCYB.2015.2412554CrossRefGoogle ScholarPubMed
Morbi, A. and Ahmadi, M., “Safely rendering small impedances in admittance-controlled haptic devices,” IEEE/ASME Trans. Mechatron. 21(3), 1272–1280 (2015).10.1109/TMECH.2015.2506994CrossRefGoogle Scholar
Yan, Y., Chen, Z., Huang, C., Chen, L. and Guo, Q., “Human-exoskeleton coupling dynamics in the swing of lower limb,” Appl. Math. Model. 104, 439–454 (2022).10.1016/j.apm.2021.12.007CrossRefGoogle Scholar
Sun, T., Chen, Z., Guo, Q. and Yan, Y., “Optimization of exoskeleton trajectory towards minimizing human joint torques,” IEEE Trans. Neural Syst. Rehabil. Eng. 33, 1231–1241 (2025).10.1109/TNSRE.2025.3553861CrossRefGoogle Scholar
Li, Z., Huang, B., Ye, Z., Deng, M. and Yang, C., “Physical human robot interaction of a robotic exoskeleton by admittance control,” IEEE Trans. Ind. Electron. 65(12), 9614–9624 (2018).10.1109/TIE.2018.2821649CrossRefGoogle Scholar
Zhan, H., Kou, J., Guo, Q., Wang, C., Chen, Z., Shi, Y. and Li, T., “Multilevel control strategy of human-exoskeleton cooperative motion with multimodal wearable training evaluation,” IEEE Trans. Control Syst. Technol. 33(2), 434–448 (2025).10.1109/TCST.2024.3477299CrossRefGoogle Scholar
Kou, J., Wang, Y., Chen, Z., Shi, Y. and Guo, Q., “Gait planning and multimodal human-exoskeleton cooperative control based on central pattern generator,” IEEE/ASME Trans. Mechatron. 30(4), 2598–2608 (2025).10.1109/TMECH.2024.3453037CrossRefGoogle Scholar
Zhan, H., Kou, J., Guo, Q., Zhang, J. and Shi, Y., “Gait trajectory planning and fixed-time fuzzy adaptive control for human-exoskeleton cooperative motion based on dynamic movement primitives,” Sci. China - Technol. Sci. 68(8), 226–239 (2025).10.1007/s11431-024-2932-yCrossRefGoogle Scholar
Yang, C., Peng, G., Li, Y., Cui, R., Cheng, L. and Li, Z., “Neural networks enhanced adaptive admittance control of optimized robot–environment interaction,” IEEE Trans. Cybern. 49(7), 2568–2579 (2018).10.1109/TCYB.2018.2828654CrossRefGoogle ScholarPubMed
Yu, X., He, W., Li, Y., Xue, C., Li, J., Zou, J. and Yang, C., “Bayesian estimation of human impedance and motion intention for human–robot collaboration,” IEEE Trans. Cybern. 51(4), 1822–1834 (2019).10.1109/TCYB.2019.2940276CrossRefGoogle Scholar
Arefeen, A. and Xiang, Y., “Artificial neural network-based control of powered knee exoskeletons for lifting tasks: Design and experimental validation,” Robotica 42(9), 2949–2968 (2024).10.1017/S0263574724001206CrossRefGoogle Scholar
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