Abstract
Soft bending pneumatic muscles (SBPMs) suffer from imprecise control due to the complicated nonlinearity, including the intrinsic rate-dependent and asymmetric hysteresis. In this work, we designed and fabricated a fiber-reinforced soft-bending pneumatic muscle (FSBPM) with high bending efficiency. A real-time visual feedback system was applied to recognize the bending angle of the FSBPM. To tackle the hysteresis problem of the FSBPM, we introduced an inverse hysteresis compensation method (IHCM) for the FSBPM, which combined the inverse hysteresis compensation with the feedback control strategy. The inverse hysteresis model was directly approximated by an echo state network (ESN). Both fixed frequency and variable frequency trajectory tracking experimental results show that compared with the traditional PID control, the proposed method effectively improves the tracking performance.
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References
Morin, A.H.: Elastic diaphragm. U.S. patent no. 2642091
Irshaidat, M., Soufian, M., Al-Ibadi, A., Nefti-Meziani, S.: A novel elbow pneumatic muscle actuator for exoskeleton arm in post-stroke rehabilitation. In: 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft). IEEE (2019). https://doi.org/10.1109/robosoft.2019.8722813
Zhou, W., Li, Y.: Modeling and analysis of soft pneumatic actuator with symmetrical chambers used for bionic robotic fish. Soft Rob. 7(2), 168–178 (2020). https://doi.org/10.1089/soro.2018.0087
Al-Fahaam, H., Davis, S., Nefti-Meziani, S.: Power assistive and rehabilitation wearable robot based on pneumatic soft actuators. In: 2016 21st international conference on methods and models in automation and robotics (MMAR), pp. 472–477. IEEE (2016)
Hao, Y., et al.: Universal soft pneumatic robotic gripper with variable effective length. In: 2016 35th Chinese Control Conference (CCC), pp. 6109–6114. IEEE (2016)
Al-Fahaam, H., Davis, S., Nefti-Meziani, S.: The design and mathematical modelling of novel extensor bending pneumatic artificial muscles (ebpams) for soft exoskeletons. Rob. Auton. Syst. 99, 63–74 (2018)
Chen, W., Xiong, C., Liu, C., Li, P., Chen, Y.: Fabrication and dynamic modeling of bidirectional bending soft actuator integrated with optical waveguide curvature sensor. Soft Rob. 6(4), 495–506 (2019)
Polygerinos, P., et al.: Modeling of soft fiber-reinforced bending actuators. IEEE Trans. Rob. 31(3), 778–789 (2015)
Zhang, L., Bao, G., Yang, Q., Ruan, J., Qi, L.: Static model of flexible pneumatic bending joint. In: 2006 9th International Conference on Control, Automation, Robotics and Vision, pp. 1–5. IEEE (2006)
Zheng, T., Branson, D.T., Guglielmino, E., Caldwell, D.G.: A 3D dynamic model for continuum robots inspired by an octopus arm. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3652–3657. IEEE (2011)
She, Y., Chen, J., Shi, H., Su, H.J.: Modeling and validation of a novel bending actuator for soft robotics applications. Soft Rob. 3(2), 71–81 (2016)
Aggarwal, A.: An improved parameter estimation and comparison for soft tissue constitutive models containing an exponential function. Biomech. Model. Mechanobiol. 16(4), 1309–1327 (2017)
Martins, S.A.M., Aguirre, L.A.: Sufficient conditions for rate-independent hysteresis in autoregressive identified models. Mech. Syst. Signal Process. 75, 607–617 (2016)
Chen, P., Bai, X.X., Qian, L.J., Choi, S.B.: An approach for hysteresis modeling based on shape function and memory mechanism. IEEE/ASME Trans. Mechatron. 23(3), 1270–1278 (2018)
Nguyen, P.B., Choi, S.B., Song, B.K.: Development of a novel diagonal-weighted preisach model for rate-independent hysteresis. Proc. Inst. Mech. Engineers Part C: J. Mech. Eng. Sci. 231(5), 961–976 (2017)
Xie, S., Mei, J., Liu, H., Wang, Y.: Hysteresis modeling and trajectory tracking control of the pneumatic muscle actuator using modified prandtl-ishlinskii model. Mech. Mach. Theory 120, 213–224 (2018)
Liu, Y., Du, D., Qi, N., Zhao, J.: A distributed parameter maxwell-slip model for the hysteresis in piezoelectric actuators. IEEE Trans. Ind. Electron. 66(9), 7150–7158 (2018)
Xie, S.L., Liu, H.T., Mei, J.P., Gu, G.Y.: Modeling and compensation of asymmetric hysteresis for pneumatic artificial muscles with a modified generalized prandtl-ishlinskii model. Mechatronics 52, 49–57 (2018)
Pop, N., Caltun, O.: Jiles-atherton magnetic hysteresis parameters identification. Acta Physica Polonica, A. 120(3), 491–496 (2011)
Shakiba, S., Ourak, M., Vander Poorten, E., Ayati, M., Yousefi-Koma, A.: Modeling and compensation of asymmetric rate-dependent hysteresis of a miniature pneumatic artificial muscle-based catheter. Mech. Syst. Signal Process. 154, 107532 (2021)
Sabarianand, D., Karthikeyan, P., Muthuramalingam, T.: A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems. Mech. Syst. Signal Process. 140, 106634 (2020)
Xu, J., Xiao, M., Ding, Y.: Modeling and compensation of hysteresis for pneumatic artificial muscles based on gaussian mixture models. Sci. China Technol. Sci. 62(7), 1094–1102 (2019)
Wu, Y., Fang, Y., Ren, X., Lu, H.: Back propagation neural networks based hysteresis modeling and compensation for a piezoelectric scanner. In: 2016 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), pp. 119–124. IEEE (2016)
Zhang, Y., Gao, J., Yang, H., Hao, L.: A novel hysteresis modelling method with improved generalization capability for pneumatic artificial muscles. Smart Mater. Struct. 28(10), 105014 (2019)
Mirikitani, D.T., Nikolaev, N.: Recursive bayesian recurrent neural networks for time-series modeling. IEEE Trans. Neural Netw. 21(2), 262–274 (2009)
Huang, J., Qian, J., Liu, L., Wang, Y., Xiong, C., Ri, S.: Echo state network based predictive control with particle swarm optimization for pneumatic muscle actuator. J. Franklin Inst. 353(12), 2761–2782 (2016)
Zhang, X., et al.: Decentralized adaptive neural approximated inverse control for a class of large-scale nonlinear hysteretic systems with time delays. IEEE Trans. Syst, Man Cybern. Syst. 49(12), 2424–2437 (2018)
Ru, H., Huang, J., Chen, W., Xiong, C.: Modeling and identification of rate-dependent and asymmetric hysteresis of soft bending pneumatic actuator based on evolutionary firefly algorithm. Mech. Mach. Theory 181, 105169 (2023). https://doi.org/10.1016/j.mechmachtheory.2022.105169
Hu, W., Mutlu, R., Li, W., Alici, G.: A structural optimisation method for a soft pneumatic actuator. Robotics 7(2), 24 (2018)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German Natl. Res. Center Inf. Technol. GMD Tech. Rep. 148(34), 13 (2001)
Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant U1913207 and by the Program for HUST Academic Frontier Youth Team. The authors would like to thank the support from these foundations.
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Ru, H., Huang, J., Wang, B. (2023). ESN-Based Control of Bending Pneumatic Muscle with Asymmetric and Rate-Dependent Hysteresis. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_1
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DOI: https://doi.org/10.1007/978-981-99-5844-3_1
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