Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-05-06T01:56:23.470Z Has data issue: false hasContentIssue false
  • English
  • Français

Bi-Level Adaptive Computed-Current Impedance Controller for Electrically Driven Robots

Published online by Cambridge University Press:  28 May 2020

Mohsen Jalaeian-F. Open the ORCID record for Mohsen Jalaeian-F. [Opens in a new window] ,
Mohammad Mehdi Fateh  and
Morteza Rahimiyan
Mohsen Jalaeian-F.*
Affiliation:
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran, E-mails: mmfateh@shahroodut.ac.ir, morteza.rahimiyan@shahroodut.ac.ir
Mohammad Mehdi Fateh
Affiliation:
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran, E-mails: mmfateh@shahroodut.ac.ir, morteza.rahimiyan@shahroodut.ac.ir
Morteza Rahimiyan
Affiliation:
Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran, E-mails: mmfateh@shahroodut.ac.ir, morteza.rahimiyan@shahroodut.ac.ir
*
*Corresponding author. E-mail: m.jalaeian@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a bi-level adaptive computed-current impedance controller for electrically driven robots. This study aims to reduce calculation complexities by utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system. Moreover, taking the dynamical effects of mechanical parts into account through the current’s feedback, external disturbances are compensated. In order to handle uncertainties, a bi-level optimization problem is formulated to obtain guaranteed stability besides the estimation convergence. An adaptation rule and its optimal tuning gain are achieved. The proposed method is applied to control of a rehabilitation robot to evaluate its performance.

Keywords

Electrically driven robots (EDRs)Rehabilitation robotBi-level optimization problem (BLOP)Bi-level adaptive controlImpedance control
Type
Articles
Information
Robotica , Volume 39 , Issue 2 , February 2021 , pp. 200 - 216
Copyright
Copyright © The Author(s), 2020. 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.)

References

Song, P., Yu, Y. and Zhang, X. A., “Tutorial survey and comparison of impedance control on robotic manipulation,” Robotica 37(5), 801–836 (2019).10.1017/S0263574718001339CrossRefGoogle Scholar
Hogan, N., “Impedance control: An approach to manipulation: Part I-theory,” J. Dyn. Syst. Meas. Control 107(1), 1–7 (1985).Google Scholar
Wu, J., Gao, J., Song, R., Li, R., Li, Y. and Jiang, L., “The design and control of a 3DOF lower limb rehabilitation robot,” Mechatronics 33(13), 13–22 (2016).CrossRefGoogle Scholar
Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B. and Xie, S. Q., “Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation,” Mechatronics 31, 132–145 (2015).10.1016/j.mechatronics.2015.04.005CrossRefGoogle Scholar
Mohan, S., Mohanta, J. K., Kurtenbach, S., Paris, J., Corves, B. and Huesing, M., “Design, development and control of a 2PRP-2PPR planar parallel manipulator for lower limb rehabilitation therapies,” Mech. Mach. Theory 112, 272–294 (2017).CrossRefGoogle Scholar
Mohanta, J. k., Mohan, S, Deepasundar, P. and Kiruba-Shankar, R., “Development and control of a new sitting-type lower limb rehabilitation robot,” Comput. Electr. Eng. 67, 330–347 (2018).Google Scholar
Ayas, M. S., Altas, I. H. and Sahin, E., “Fractional order based trajectory tracking control of an ankle rehabilitation robot,” Trans. Inst. Meas. Control 40(2), 550–564 (2018).CrossRefGoogle Scholar
van Kammen, K., Boonstra, A. M., van der Woude, L. H. V., Visscher, C., Reinders-Messelink, H. A. and R. den Otter, “Lokomat guided gait in hemiparetic stroke patients: The effects of training parameters on muscle activity and temporal symmetry,” Disabil. Rehabil. 11, 1–9 (2019).Google Scholar
Guzmán-Valdivia, C. H., Blanco-Ortega, A., Oliver-Salazar, M. A., Gómez-Becerra, F. A. and Carrera-Escobedo, J. L., “HipBot - The design, development and control of a therapeutic robot for hip rehabilitation,” Mechatronics 30, 55–64 (2015).CrossRefGoogle Scholar
Jamwal, P. K., Hussain, S., Ghayesh, M. H. and Rogozina, S. V., “Impedance control of an intrinsically compliant parallel Ankle rehabilitation robot,” IEEE Trans. Ind. Electron. 63(6), 3638–3647 (2016).CrossRefGoogle Scholar
Guo, C., Guo, S., Ji, J. and Xi, F., “Iterative learning impedance for lower limb rehabilitation robot,” J. Healthc. Eng., Article ID 6732459, 9 p. (2017). doi:10.1155/2017/6732459.CrossRefGoogle Scholar
Teramae, T., Noda, T. and Morimoto, J., “EMG-based model predictive control for physical human–robot interaction: Application for assist-as-needed control,” IEEE Robot. Autom. Lett. 3(1), 210–217 (2018).10.1109/LRA.2017.2737478CrossRefGoogle Scholar
Akdoğan, E., Aktan, M. E., Koru, A. T., Selçuk Arslan, M., Atlhan, M. and Kuran, B., “Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: Performance analysis and clinical results,” Mechatronics 49, 77–91 (2018).CrossRefGoogle Scholar
Khoshdel, V. and Moeenfard, H., “Variable impedance control for rehabilitation robot using interval type-2 fuzzy logic,” Int. J. Robot. Theory Appl. 4(3), 46–54 (2015).Google Scholar
Lewis, F. L., Dawson, D. M. and Abdallah, C. T., Control of Robot Manipulators, 1st edn (Prentice Hall PTR, Upper Saddle River, NJ, USA, 1993).Google Scholar
Liu, C., Cheah, C. C. and Slotine, J. J. E., “Adaptive Jacobian tracking control of rigid-link electrically driven robots based on visual task-space information,” Automatica 42(9), 1461–1501 (2006).CrossRefGoogle Scholar
Ahmadipour, M., Khayatian, A. and Dehghani, M., “Adaptive control of rigid-link electrically driven robots with parametric uncertainties in kinematics and dynamics and without acceleration measurements,” Robotica 32(7), 1153–1169 (2014).10.1017/S0263574713001203CrossRefGoogle Scholar
Xu, L., Hu, Q. and Zhang, Y., “L2 performance control of robot manipulators with kinematics, dynamics and actuator uncertainties,” Int. J. Robust Nonlinear Control 27(6), 875–893 (2017).CrossRefGoogle Scholar
Ahmadi, S. M. and Fateh, M. M., “On the Taylor series asymptotic tracking control of robots,” Robotica 37(3), 405–427 (2019).10.1017/S0263574718001078CrossRefGoogle Scholar
Fateh, M. M., “On the voltage-based control of robot manipulators,” Int. J. Control Autom. Syst. 6(5), 702–712 (2008).Google Scholar
Fateh, M. M. and Babaghasabha, R., “Impedance control of robots using voltage control strategy,” Nonlinear Dyn. 74(1–2), 277–286 (2013).10.1007/s11071-013-0964-yCrossRefGoogle Scholar
Fateh, M. M. and Khoshdel, V., “Voltage-based adaptive impedance force control for a lower-limb rehabilitation robot,” Adv. Robot. 29(15), 1003–1013 (2015).CrossRefGoogle Scholar
Khoshdel, V., Akbarzadeh, A., Naghavi, N., Sharifnezhad, A. and Souzanchi-Kashani, M., “sEMG-based impedance control for lower-limb rehabilitation robot,” Intell. Serv. Robot. 11(1), 97–108 (2017).CrossRefGoogle Scholar
Souzanchi-K, M.., Arab, A., Akbarzadeh-T, M. R. and Fateh, M. M., “Robust impedance control of uncertain mobile manipulators using time-delay compensation,” IEEE Trans. Syst, Control . Technol. 26(6), 1942–1953 (2018).CrossRefGoogle Scholar
Shahhosseini, M., “Impedance control of robots using voltage control strategy revisited,” J. Mod. Process. Manuf. Prod. 7(1), 49–66 (2019).Google Scholar
Izadbakhsh, A., Kheirkhahan, P. and Khorashadizadeh, S., “FAT-based robust adaptive control of electrically driven robots in interaction with environment,” Robotica 37(5), 779–800 (2019).CrossRefGoogle Scholar
Nazmara, G., Fateh, M. M. and Ahmadi, S. M., “A model-reference impedance control of robot manipulators using an adaptive fuzzy uncertainty estimator,” Int. J. Comput. Intell. Syst. 11(1), 979–990 (2018).CrossRefGoogle Scholar
Fliege, J. and Vicente, L. N., “Multicriteria approach to bilevel optimization,” J. Optim. Theory Appl. 131(2), 209–225 (2006).Google Scholar
von Stackelberg, H., The Theory of the Market Economy, (Oxford University Press, New York, NY, USA, 1952).Google Scholar
Dempe, S., Bilevel optimization: Theory, algorithms and applications, TU Bergakademie Freiberg (2018).Google Scholar
Sinha, A., Malo, P. and Deb, K., “A review on bilevel optimization: From classical to evolutionary approaches and applications,” IEEE Trans. Evol. Comput. 22(2), 276–295 (2018).10.1109/TEVC.2017.2712906CrossRefGoogle Scholar
Lee, J. H., “Model predictive control: Past, present and future”, Comput. Chem. Eng. 23(4–5), 667–682 (1999).Google Scholar
Chen, Y. and Hu, M., “Swarm intelligence-based distributed stochastic model predictive control for transactive operation of networked building clusters”, Energy Buildi. 198(1), 207–215 (2019).CrossRefGoogle Scholar
Saraeian, S., Shirazi, B. and Motameni, H., “Optimal autonomous architecture for uncertain processes management”, Inf. Sci. 501, 84–99 (2019).Google Scholar
Tilahun, S. L., “Feasibility reduction approach for hierarchical decision making with multiple objectives”, Operat. Res. Perspect. 6 (2019).CrossRefGoogle Scholar
Benita, F., Dempe, S. and Mehlitz, P., “Bilevel optimal control problems with pure state constraints and finite-dimensional lower level”, Soc. Indus. Appl. Math. 26(1), 564–588 (2016).Google Scholar
Du, G., Zhang, Y., Liu, X., Jiao, R. J., Xia, Y. and Li, Y., “A review of leader-follower joint optimization problems and mathematical models for product design and development”, Int. J. Adv. Manuf. Tech. 103, 3405–3424 (2019).10.1007/s00170-019-03612-6CrossRefGoogle Scholar
DAmato, E., Mattei, M. and Notaro, I., “Distributed reactive model predictive control for collision avoidance of unmanned aerial vehicles in civil airspace”, J. Intell. Robot. Syst. 97, 185–203 (2020).CrossRefGoogle Scholar
Izadbakhsh, A. and Kheirkhahan, P., “On the voltage-based control of robot manipulators revisited,” Int. J. Control Autom. Syst. 16(4), 1887–1894 (2018).10.1007/s12555-017-0035-0CrossRefGoogle Scholar
Shanmugasundram, R., Zakaraiah, K. M. and Yadaiah, N., “Effect of parameter variations on the performance of direct current (DC) servomotor drives,” JVC/J. Vib Control 19(10), 1575–1586 (2013).CrossRefGoogle Scholar
Tao, G., “Multivariable adaptive control: A survey,” Automatica 50(11), 2737–2764 (2014).Google Scholar
Arteaga-Pérez, M. A., Pliego-Jiménez, J. and Romero, J. G., “Experimental results on the robust and adaptive control of robot manipulators without velocity measurements,” IEEE Trans. Syst, Control. Tech. (2020).Google Scholar
Zeng, C., Shen, D. and Wang, J., “Adaptive learning tracking for robot manipulators with varying trial lengths,” J. Franklin Inst. 356(12), 5993–6014 (2019).10.1016/j.jfranklin.2019.04.034CrossRefGoogle Scholar
Jamwal, P. K., Hussain, S., Ghayesh, M. H. and Rogozina, S. V., “Adaptive impedance control of parallel ankle rehabilitation robot,” J. Dyn. Syst. Meas. Control 139(11), 7 p. (2017).10.1115/1.4036560CrossRefGoogle Scholar
Spong, M. W., Hutchinson, S. and Vidyasagar, M., “Robot modeling and control,” IEEE Control Syst. 26, 113–115 (2006).Google Scholar
Izadbakhsh, A. and Fateh, M. M., “Robust Lyapunov-based control of flexible-joint robots using voltage control strategy,” Arab. J. Sci. Eng. 39(4), 3111–3121 (2014).Google Scholar
Akdoğan, E. and Adli, M. A., “The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot,” Mechatronics 21(3), 509–522 (2011).CrossRefGoogle Scholar
Huang, H., Crouch, D. L., Liu, M., Sawicki, G. S. and Wang, D., “A cyber expert system for auto-tuning powered prosthesis impedance control parameters,” Ann. Biomed. Eng. 44(5), 1613–1624 (2016).CrossRefGoogle ScholarPubMed
Eiammanussakul, T. and Sangveraphunsiri, V., “A lower limb rehabilitation robot in sitting position with a review of training activities,” J. Healthc. Eng., Article ID 1927807, 18 p. (2018). doi:10.1155/2018/1927807.Google ScholarPubMed
Hussain, S., Jamwal, P. K., Ghayesh, M. H. and Xie, S. Q., “Assist-as-needed control of an intrinsically compliant robotic gait training orthosis,” IEEE Trans. Ind. Electron. 64(2), 1675–1685 (2017).CrossRefGoogle Scholar
Lee, J., Chang, P. H. and Jamisola, R. S., “Relative impedance control for dual-arm robots performing asymmetric bimanual tasks,” IEEE Trans. Ind. Electron. 61(7), 3786–3796 (2014).CrossRefGoogle Scholar
Rahimi, H. N., Howard, I. and Cui, L., “Neural impedance adaption for assistive human–robot interaction,” Neurocomputing 290, 50–59 (2018).10.1016/j.neucom.2018.02.025CrossRefGoogle Scholar
Taherifar, A., Vossoughi, G. and Ghafari, A. S., “Assistive-compliant control of wearable robots for partially disabled individuals,” Control Eng. Pract. 74, 177–190 (2018).10.1016/j.conengprac.2018.02.004CrossRefGoogle Scholar
Khalil, H. K., Nonlinear Systems, 3rd edn (Prentice Hall, New Jersey, 2001).Google Scholar
Zhang, X. and Lewis, F. L., “Cooperative output regulation of heterogeneous multi-agent systems based on passivity,” Int. J. Syst. Sci. 49(16), 3418–3430 (2018).10.1080/00207721.2018.1542044CrossRefGoogle Scholar
Zhang, Y., Li, S., Zou, J. and Khan, A. H., “A passivity-based approach for kinematic control of manipulators with constraints,” IEEE Trans. Ind. Inf. 16(5), 3029–3038 (2020).Google Scholar
Kim, M. J., Leea, W., Choic, J. Y., Chungc, G., Hand, K. L., Choie, I. S., Ottb, C. and Chung, W. K., “A passivity-based nonlinear admittance control with application to powered upper-limb control under unknown environmental interactions,” IEEE/ASME Trans. Mechatron. 24(4), 1473–1484 (2019).Google Scholar
Calanca, A., Muradore, R. and Fiorini, P., “Impedance control of series elastic actuators: Passivity and acceleration-based control,” Mechatronics 47, 37–48 (2017).Google Scholar
Al-Shuka, H. F. N. and Song, R., “Decentralized adaptive partitioned approximation control of high degrees-of-freedom robotic manipulators considering three actuator control modes,” Int. J. Dyn. Control 7(2), 744–757 (2018).Google Scholar
Zou, D., Gong, C. and Xu, Z., “Signal detection under short-interval sampling of continuous waveforms for optical wireless scattering communication,” IEEE Trans. Wirel. Commun. 17(5), 3431–3443 (2018).10.1109/TWC.2018.2812161CrossRefGoogle Scholar
Lin, F. J., Chen, S. G. and Sun, I. F., “Intelligent sliding-mode position control using recurrent wavelet fuzzy neural network for electrical power steering system,” Int. J. Fuzzy Syst. 19, 1344–1361 (2017).Google Scholar
Baringo, A. and Baringo, L., “A stochastic adaptive robust optimization approach for the offering strategy of a virtual power plant,” IEEE Trans. Syst, Power . 32(5), 3492–3504 (2017).Google Scholar
Zeng, B. and Zhao, L., “Solving two-stage robust optimization problems using a column-and- constraint generation method,” Oper. Res. Lett. 41(5), 457–461 (2013).CrossRefGoogle Scholar