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Hybrid Adaptive Robust Control Based on CPG and ZMP for a Lower Limb Exoskeleton

Published online by Cambridge University Press:  02 June 2020

Majid Mokhtari,
Mostafa TaghizadehOpen the ORCID record for Mostafa Taghizadeh[Opens in a new window] and
Mahmood Mazare
Majid Mokhtari
Affiliation:
School of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
Mostafa Taghizadeh*
Affiliation:
School of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
Mahmood Mazare
Affiliation:
School of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
*
*Corresponding author. E-mail: mo_taghizadeh@sbu.ac.ir
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Summary

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In this paper, hybrid control of central pattern generators (CPGs), along with an adaptive supper-twisting sliding mode (ASTSM) control based on supper-twisting state observer, is proposed to guard against disturbances and uncertainties. Rhythmic and coordinated signals are generated using CPGs. In addition, to overcome the chattering of conventional sliding mode, supper-twisting sliding mode has been applied. The ASTSM method triggers sliding variables, and its derivatives tend to zero continuously in the presence of the uncertainties. Moreover, to acquire maximum stability, the desired trajectory of the upper limb based on zero moment point criterion is designed.

Keywords

ExoskeletonAdaptive CPGAdaptive super-twisting sliding mode controllerSuper-twisting observer algorithmZMP

Information

Type
Articles
Information
Robotica , Volume 39 , Issue 2 , February 2021, pp. 181 - 199
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

References

Bogue, R., “Exoskeletons and robotic prosthetics: a review of recent developments,” Indus. Robot: Int. J. 36(5), 421–427 (2009).10.1108/01439910910980141CrossRefGoogle Scholar
Jezernik, S., Colombo, G., Keller, T., Frueh, H. and Morari, M., “Robotic orthosis lokomat: A rehabilitation and research tool,” Neuromodulation: Technol. Neural Interface, 6(2), 108–115 (2003).10.1046/j.1525-1403.2003.03017.xCrossRefGoogle ScholarPubMed
Duschau-Wicke, A., Thomas, B., Lars, L., and Robert, R., “Adaptive support for patient-cooperative gait rehabilitation with the lokomat,” In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IEEE, 2008 pp. 2357–2361).Google Scholar
Kazerooni, H., Steger, R. and Huang, L., “Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX),” Int. J. Robot. Res. 25(5–6), 561–573 (2006).Google Scholar
Springer Handbook of Robotics, (Siciliano, B. and Khatib, O., eds.) (Springer, Wurzburg, 2016).Google Scholar
Yan, T., Cempini, M., Maria Oddo, C. and Vitiello, N., “Review of assistive strategies in powered lower-limb orthoses and exoskeletons,” Rob. Auton. Syst. 64(1), 120–136 (2015).10.1016/j.robot.2014.09.032CrossRefGoogle Scholar
Qu, Z. and Dorsey, J., “Robust tracking control of robots by a linear feedback law,” IEEE Trans. Autom. Control 36(9), 1081–1084 (1991).10.1109/9.83543CrossRefGoogle Scholar
Hong, Y., “Finite-time stabilization and stabilizability of a class of controllable systems,” Syst. Control Lett. 46(4), 231–236 (2002).10.1016/S0167-6911(02)00119-6CrossRefGoogle Scholar
Venkataraman, S. T. and Gulati, S., “Terminal sliding modes: a new approach to nonlinear control synthesis,” In: Fifth International Conference on Advanced Robotics’ Robots in Unstructured Environments, Pisa, Italy (1991) pp. 443–448.Google Scholar
Bartolini, G., Ferrara, A., Levant, A. and Usai, E., “On second order sliding mode controllers,” In: Variable Structure Systems, Sliding Mode and Nonlinear Control (Springer, London, 1999) pp. 329–350.Google Scholar
Colet, E. F., Colet, E. F. and Fridman, L. M., Advances in Variable Structure and Sliding Mode Control. (Edwards, C. and Fridman, L., eds.) vol. 334. (Springer, Berlin, 2006).Google Scholar
Sabanovic, A., Fridman, L. M., Spurgeon, S. and Spurgeon, S. K., Variable Structure Systems: From Principles to Implementation, vol. 66 (London, IET, 2004).Google Scholar
Bartolini, G., Ferrara, A., Usai, E. and Utkin, V. I., “On multi-input chattering-free second-order sliding mode control,” IEEE Trans. Autom. Control 45(9), 1711–1717 (2000).10.1109/9.880629CrossRefGoogle Scholar
Shtessel, Y., Edwards, C., Fridman, L. and Levant, A., Sliding Mode Control and Observation (Springer, New York, New York, 2014).Google Scholar
Zebbar, M., Messlem, Y., Gouichiche, A. and Tadjine, M., “Super-twisting sliding mode control and robust loop shaping design of RO desalination process powered by PV generator,” Desalination 458, 122–135 (2019).10.1016/j.desal.2019.02.011CrossRefGoogle Scholar
Moreno, J. A. and Osorio, M., “A Lyapunov approach to second-order sliding mode controllers and observers,” In: 2008 47th IEEE Conference on Decision and Control, IEEE (2008) pp. 2856–2861.Google Scholar
Zargham, F. and Mazinan, A. H., “Super-twisting sliding mode control approach with its application to wind turbine systems,” Energy Syst. 10(1), 211–229 (2019).Google Scholar
Liang, D., Li, J. and Qu, R., “Super-twisting algorithm based sliding-mode observer with online parameter estimation for sensorless control of permanent magnet synchronous machine,” In: 2016 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE (2016) pp. 1–8.Google Scholar
Shtessel, Y., Taleb, M. and Plestan, F., “A novel adaptive-gain supertwisting sliding mode controller: Methodology and application,” Automatica 48(5), 759–769 (2012).10.1016/j.automatica.2012.02.024CrossRefGoogle Scholar
Massah, A., Zamani, A., Salehinia, Y., Aliyari Sh, M. and Teshnehlab, M., “A hybrid controller based on CPG and ZMP for biped locomotion,” J. Mech. Sci. Technol. 27(11), 3473–3486 (2013).10.1007/s12206-013-0871-7CrossRefGoogle Scholar
Crespi, A. and Ijspeert, A. J., “Online optimization of swimming and crawling in an amphibious snake robot,” IEEE Trans. Rob. 24(1), 75–87 (2008).10.1109/TRO.2008.915426CrossRefGoogle Scholar
Liu, C., Chen, Q. and Wang, D., “CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots,” IEEE Trans. Syst. Man Cyber. Part B (Cybernetics) 41(3), 867–880 (2011).Google ScholarPubMed
Ijspeert, A. J., “Central pattern generators for locomotion control in animals and robots: a review,” Neural Networks 21(4), 642–653 (2008).CrossRefGoogle ScholarPubMed
Gui, K., Liu, H. and Zhang, D., “Toward multimodal human–robot interaction to enhance active participation of users in gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2054–2066 (2017).10.1109/TNSRE.2017.2703586CrossRefGoogle ScholarPubMed
Ajayi, M. O., “Modelling and control of actuated lower limb exoskeletons: a mathematical application using central pattern generators and nonlinear feedback control techniques.” PhD diss., Université Paris-Est (2016).Google Scholar
Latham, P. W., “A simulation study of bipedal walking robots: modeling, walking algorithms, and neural network control.” (1992).Google Scholar
Ijspeert, A. J., “Central pattern generators for locomotion control in animals and robots: a review,” Neural Networks 21(4), 642–653 (2008).CrossRefGoogle ScholarPubMed
Baydin, A. G., “Evolution of central pattern generators for the control of a five-link bipedal walking mechanism,” Paladyn, J. Behav. Robot. 3(1), 45–53 (2012).Google Scholar
Wang, M., Dong, H., Li, X., Zhang, Y. and Yu, J., “Control and optimization of a bionic robotic fish through a combination of CPG model and PSO,” Neurocomputing 337, 144–152 (2019).10.1016/j.neucom.2019.01.062CrossRefGoogle Scholar
Kalani, H., Moghimi, S. and Akbarzadeh, A., “Toward a bio-inspired rehabilitation aid: sEMG-CPG approach for online generation of jaw trajectories for a chewing robot,” Biomed. Signal Process. Control 51, 285–295 (2019).10.1016/j.bspc.2019.02.022CrossRefGoogle Scholar
Hemami, H. and Golliday, C. L. Jr., “The inverted pendulum and biped stability,” Math. Biosci. 34(1–2), 95–110 (1977).10.1016/0025-5564(77)90038-4CrossRefGoogle Scholar
Moosavian, S., Ali, A., Alipour, K. and Bahramzadeh, Y., “Dynamics modeling and tip-over stability of suspended wheeled mobile robots with multiple arms,” In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (2007) pp. 1210–1215.Google Scholar
Takhmar, A., Alghooneh, M., Alipour, K., Ali, S. and Moosavian, A., “MHS measure for postural stability monitoring and control of biped robots,” In: 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE (2008) pp. 400–405.Google Scholar
Mousavi, P. N. and Bagheri, A., “Mathematical simulation of a seven link biped robot on various surfaces and ZMP considerations,” Appl. Math. Modell. 31(1), 18–37 (2007).10.1016/j.apm.2006.06.018CrossRefGoogle Scholar
Khalili, M., Kharrat, R., Salahshoor, K. and Sefat, M. H., “Global dynamic harmony search algorithm: GDHS,” Appl. Math. Comput. 228, 195–219 (2014).Google Scholar
Kawamoto, H. and Sankai, Y., “Power assist method based on phase sequence and muscle force condition for HAL,” Adv. Rob. 19(7), 717–734 (2005).CrossRefGoogle Scholar
Kyaw, P. K., Sandar, K., Khalid, M., Juan, W., Li, Y., Chen, Z., Opportunities in robotic exoskeletons hybrid assistive limbSUIT (MT5009), Robotic Exoskeletons: Becoming Economically Feasible, Nov 21, (2013).Google Scholar
Craig, J. J., Introduction to Robotics: Mechanics and Control, Pearson Education India (Hall, London, 2005), pp. 85–310.Google Scholar
Bay, J. S. and Hemami, H., “Modeling of a neural pattern generator with coupled nonlinear oscillators,” IEEE Trans. Biomed. Eng. 4(1), 297–306 (1987).10.1109/TBME.1987.326091CrossRefGoogle Scholar
Aphiratsakun, N., Chairungsarpsook, K. and Parnichkun, M., “ZMP based gait generation of AIT’s Leg Exoskeleton,” In: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), IEEE vol. 5, (2010), pp. 886–890.Google Scholar
Yu, X. and Efe, M. Ö., Recent advances in sliding modes: from control to intelligent mechatronics, vol. 24 (2015).10.1007/978-3-319-18290-2CrossRefGoogle Scholar
Levant, A., “Sliding order and sliding accuracy in sliding mode control,” Int. J. Control 58(6), 1247–1263 (1993).CrossRefGoogle Scholar
Levant, A., “Robust exact differentiation via sliding mode technique,” Automatica 34(3), 379–384 (1998).Google Scholar
Goswami, A., “Postural stability of biped robots and the foot-rotation indicator (FRI) point,” Int. J. Rob. Res. 18(6), 523–533 (1999).CrossRefGoogle Scholar