Abstract
In recent years, various series elastic actuators (SEAs) have been proposed to enhance the flexibility and safety of wearable exoskeletons. This paper proposes an SEA composed of wave springs and installs it at human-robot interaction port. Considering the hysteresis nonlinear characteristics of the SEA, displacement-force models of the SEA are established based on long short-term memory (LSTM) model and T-S fuzzy model in a nonlinear auto-regression moving average with exogenous input (NARMAX) structure. Based on the established models, the SEA can effectively serve as an interaction force sensor. Subsequently, the SEA is integrated into an elbow exoskeleton, and a compliant admittance controller is designed based on the displacement-force model. Experimental results demonstrate that the proposed approach effectively enhances the flexibility of human-robot interaction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiao, H., Wu, Y.X., Zhong, S.L., Yin, P.J., Chen, J.H.: Brain-inspired intelligent robotics: theoretical analysis and systematic application. Mach. Intell. Res. 20(1), 1–18 (2023)
Weiss, A., Wortmeier, A.K., Kubicek, B.: Robots in industry 4.0: a roadmap for future practice studies on human-robot collaboration. IEEE Trans. Hum. Mach. Syst. 51(4), 335–345 (2021)
Cao, R., Cheng, L., Li, H.: Passive model predictive impedance control for safe physical human-robot interaction. IEEE Trans. Cogn. Dev. Syst. (2023). https://doi.org/10.1109/TCDs.2023.3275217
Cheng, L., Xia, X.: A survey of intelligent control of upper limb rehabilitation exoskeleton. Robot 44(6), 750–768 (2022)
Qian, W., et al.: CURER: a lightweight cable-driven compliant upper limb rehabilitation exoskeleton robot. IEEE/ASME Trans. Mechatron. 28(3), 1730–1741 (2023)
Liang, J., Zhang, Q., Liu, Y., Wang, T., Wan, G.: A review of the design of load-carrying exoskeletons. Sci. China Technol. Sci. 65(9), 2051–2067 (2022)
Samper-Escudero, J.L., Coloma, S., Olivares-Mendez, M.A., Gonzalez, M.A.S.U., Ferre, M.: A compact and portable exoskeleton for shoulder and elbow assistance for workers and prospective use in space. IEEE Trans. Hum. Mach. Syst. 53(4), 668–677 (2022)
Grazi, L., Trigili, E., Proface, G., Giovacchini, F., Crea, S., Vitiello, N.: Design and experimental evaluation of a semi-passive upper-limb exoskeleton for workers with motorized tuning of assistance. IEEE Trans. Neural Syst. Rehabil. Eng. 28(10), 2276–2285 (2020)
Zimmermann, Y., et al.: Digital Guinea Pig: merits and methods of human-in-the-loop simulation for upper-limb exoskeletons. In: 2022 International Conference on Rehabilitation Robotics, Rotterdam, Netherlands, pp. 1–6, IEEE (2022)
Zhang, Y., Cheng, L., Cao, R., Li, H., Yang, C.: A neural network based framework for variable impedance skills learning from demonstrations. Robot. Auton. Syst. 160, 104312 (2023)
Li, J.F., Cao, Q., Dong, M.J., Zhang, C.: Compatibility evaluation of a 4-DOF ergonomic exoskeleton for upper limb rehabilitation. Mech. Mach. Theor. 156, 104146 (2021)
He, C., Xiong, C.H., Chen, Z.J., Fan, W., Huang, X.L., Fu, C.L.: Preliminary assessment of a postural synergy-based exoskeleton for post-stroke upper limb rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1795–1805 (2021)
Ang, B.W.K., Yeow, C.H.: Design and modeling of a high force soft actuator for assisted elbow flexion. IEEE Robot. Autom. Lett. 5(2), 3731–3736 (2020)
Jarrett, C., McDaid, A.J.: Robust control of a cable-driven soft exoskeleton joint for intrinsic human-robot interaction. IEEE Trans. Neural Syst. Rehabil. Eng. 25(7), 976–986 (2017)
Gao, G., Liang, J., Liarokapis, M.: Mechanically programmable jamming based on articulated mesh structures for variable stiffness robots. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyoto, Japan, pp. 11586–11593. IEEE (2022)
Trigili, E., et al.: Design and experimental characterization of a shoulder elbow exoskeleton with compliant joints for post-stroke rehabilitation. IEEE/ASME Trans. Mechatron. 24(4), 1485–1496 (2019)
Li, J., Li, S.Q., Tian, G.H., Shang, H.C.: Muscle tension training method for series elastic actuator (SEA) based on gain-scheduled method. Robot. Auton. Syst. 121, 103253 (2019)
Li, S.H., Shi, Y., Hu, L.N., Sun, Z.: A generalized model predictive control method for series elastic actuator driven exoskeleton robots. Comput. Electr. Eng. 94, 107328 (2021)
Sun, N., Cheng, L., Xia, X.: Design and hysteresis modeling of a miniaturized elastomer-based clutched torque sensor. IEEE Trans. Instrum. Measur. 71, 7501409 (2022)
Lin, Y.J., Chen, Z., Yao, B.: Decoupled torque control of series elastic actuator with adaptive robust compensation of time-varying load-side dynamics. IEEE Trans. Industr. Electron. 67(7), 5604–5614 (2019)
Aguirre-Ollinger, G., Yu, H.Y.: Lower-limb exoskeleton with variable-structure series elastic actuators: phase-synchronized force control for gait asymmetry correction. IEEE Trans. Rob. 37(3), 763–779 (2020)
Pan, J., et al.: NESM-\(\gamma \): an upper-limb exoskeleton with compliant actuators for clinical deployment. IEEE Robot. Autom. Lett. 7(3), 7708–7715 (2022)
Chen, T., Casas, R., Lum, P.S.: An elbow exoskeleton for upper limb rehabilitation with series elastic actuator and cable-driven differential. IEEE Trans. Rob. 35(6), 1464–1474 (2019)
Wu, K.Y., Su, Y.Y., Yu, Y.L., Lin, C.H., Lan, C.C.: A 5-degrees-of-freedom lightweight elbow-wrist exoskeleton for forearm fine-motion rehabilitation. IEEE/ASME Trans. Mechatron. 24(6), 2684–2695 (2019)
Buerger, S.P., Hogan, N.: Complementary stability and loop shaping for improved human-robot interaction. IEEE Trans. Rob. 23(2), 232–244 (2007)
Zou, Y., Cheng, L., Li, Z.: A multimodal fusion model for estimating human hand force: comparing surface electromyography and ultrasound signals. IEEE Robot. Autom. Mag. 29(4), 10–24 (2022)
Chen, S., Billings, S.A.: Representation of non-linear systems: the NARMAX model. Int. J. Control 49(3), 1012–1032 (1999)
Liu, W., Cheng, L., Hou, Z.G., Yu, J., Tan, M.: An inversion-free predictive controller for piezoelectric actuators based on a dynamic linearized neural network model. IEEE/ASME Trans. Mechatron. 21(1), 214–226 (2016)
Xia, X.Z., Cheng, L.: Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles. Sci. China Technol. Sci. 64(10), 2272–2280 (2021). https://doi.org/10.1007/s11431-021-1887-6
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Gao, H., et al.: Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections. Sci. China Inf. Sci. 64(7), 1–13 (2021). https://doi.org/10.1007/s11432-020-3071-8
Acknowledgements
This work is supported by National Key Research & Development Program (Grant No. 2022YFB4703204) and National Natural Science Foundation of China (Grant Nos. 62025307 and 62311530097).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xia, X., Han, L., Li, H., Zhang, Y., Liu, Z., Cheng, L. (2024). A Compliant Elbow Exoskeleton with an SEA at Interaction Port. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14450. Springer, Singapore. https://doi.org/10.1007/978-981-99-8070-3_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-8070-3_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8069-7
Online ISBN: 978-981-99-8070-3
eBook Packages: Computer ScienceComputer Science (R0)