Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T08:18:37.560Z Has data issue: false hasContentIssue false
  • English
  • Français

Novel adaptive backstepping control for uncertain manipulator robots using state and output feedback

Published online by Cambridge University Press:  23 November 2021

Brahim Brahmi Open the ORCID record for Brahim Brahmi [Opens in a new window] ,
Maarouf Saad ,
Claude El-Bayeh ,
Mohammad Habibur Rahman  and
Abdelkrim Brahmi
Brahim Brahmi*
Affiliation:
Electrical and Computer Engineering Department, Miami University, Oxford, OH, USA
Maarouf Saad
Affiliation:
Electrical Engineering Department, College Ahuntsic, Montreal, Quebec, Canada
Claude El-Bayeh
Affiliation:
Concordia University, Montreal
Mohammad Habibur Rahman
Affiliation:
Mechanical Engineering Department, University of Wisconsin-Milwaukee, Wisconsin-Milwaukee, WI, USA
Abdelkrim Brahmi
Affiliation:
Ecole de technologie superieure, Montreal, QuébecH3S1E3, Canada
*
*Corresponding author. E-mail: brahim.brahmi@mcgil.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, a new adaptive control strategy, based on the Modified Function Approximation Technique, is proposed for a manipulator robot with unknown dynamics. This novel strategy benefits from the backstepping control approach and the use of state and output feedback. Unlike the conventional Function Approximation Technique approach, the use of basis functions to approximate the dynamic parameters is completely eliminated in the proposed scheme. Another improvement is eliminating the need to measure velocity by means of integrating a high-order sliding mode observer. Furthermore, utilizing the Lyapunov function theory, it is demonstrated that all controller signals are uniformly ultimately bounded in the closed-loop form. Lastly, simulation and comparative studies are carried out to validate the effectiveness of the proposed control approach.

Keywords

function approximation techniquebackstepping controladaptive controlstate observerunknown dynamics
Type
Research Article
Information
Robotica , Volume 40 , Issue 5 , May 2022 , pp. 1326 - 1344
Copyright
© The Author(s), 2021. 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

Xu, L. D., Xu, E. L. and Li, L., “Industry 4.0: State of the art and future trends,” Int. J. Product. Res. 56(8), 2941–2962 (2018).CrossRefGoogle Scholar
Atkeson, C. G., Benzun, P. B., Banerjee, N., D. Berenson, C. P. Bove, X. Cui, M. DeDonato, R. Du, S. Feng, P. Franklin and M. Gennert, “What Happened at the Darpa Robotics Challenge Finals,” In: The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue (Springer, 2018), pp. 667–684.CrossRefGoogle Scholar
Bautista, A. J. and Wane, S. O., “Atlas Robot: A Teaching Tool for Autonomous Agricultural Mobile Robotics,” 2018 International Conference on Control, Automation and Information Sciences (ICCAIS) (IEEE, 2018), pp. 264–269.CrossRefGoogle Scholar
Liu, D., Sun, M. and Qian, D., “Structural Design and Gait Simulation of Bionic Quadruped Robot,” 2018 International Conference on Advanced Mechatronic Systems (ICAMechS) (IEEE, 2018), pp. 16–20.CrossRefGoogle Scholar
Ferguson, J. M., Cai, L. Y., Reed, A., M. Siebold, S. De, S. Duke Herrell and R. J. Webster, “Toward Image-Guided Partial Nephrectomy with the da Vinci Robot: Exploring Surface Acquisition Methods for Intraoperative Re-Registration,” In: Medical Imaging 2018 : Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576, 1057609 (International Society for Optics and Photonics, 2018).CrossRefGoogle Scholar
Sadeque, M. and Balachandran, S. K., “Overview of Medical Device Processing,” In: Trends in Development of Medical Devices (2020), pp. 177–188.Google Scholar
Clancy, G., Peeters, D., Oliveri, V., D. Jones, R. M. O’Higgins and P. M. Weaver, “A study of the influence of processing parameters on steering of carbon fibre/peek tapes using laser-assisted tape placement,” Compos. Part B Eng. 163, 243–251 (2019).Google Scholar
He, W., Li, Z., Dong, Y. and Zhao, T. et al. , “Design and adaptive control for an upper limb robotic exoskeleton in presence of input saturation,” IEEE Trans. Neural Networks Learn. Syst. 30(1), 97–108 (2018).Google ScholarPubMed
Al-Shuka, H. F. and Song, R., “Hybrid Regressor and Approximation-based Adaptive Control of Robotic Manipulators with Contact-Free Motion,” 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (IEEE, 2018), pp. 325–329.CrossRefGoogle Scholar
Rahman, M. H., Saad, M., Kenné, J.-P. and Archambault, P. S., “Control of an exoskeleton robot arm with sliding mode exponential reaching law,” Int. J. Control Autom. Syst. 11(1), 92–104 (2013).CrossRefGoogle Scholar
Ueda, J., Ming, D., Krishnamoorthy, V., Shinohara, M. and Ogasawara, T., “Individual muscle control using an exoskeleton robot for muscle function testing,” IEEE Trans. Neural Syst. Rehabil. Eng. 18(4), 339–350 (2010).CrossRefGoogle ScholarPubMed
Lee, B.-K., Lee, H.-D., J.-y. Lee, K. Shin, J.-S. Han and C.-S. Han, “Development of Dynamic Model-based Controller for Upper Limb Exoskeleton Robot,” 2012 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2012), pp. 3173–3178.Google Scholar
Krstic, M., Kanellakopoulos, I., Kokotovic, P. V. and I. Kanellakopoulos et al. , Nonlinear and Adaptive Control Design, vol. 222 (Wiley, New York, 1995).Google Scholar
Khan, A. M., D.-w. Yun, K. M. Zuhaib, J. Iqbal, R.-J. Yan, F. Khan and C. Han, “Estimation of desired motion intention and compliance control for upper limb assist exoskeleton,” Int. J. Control Autom. Syst. 15(2), 802–814 (2017).CrossRefGoogle Scholar
Khan, A. M., Usman, M., Ali, A., F. Khan, S. Yaqub and C. Han, “Muscle circumference sensor and model reference-based adaptive impedance control for upper limb assist exoskeleton robot,” Adv. Rob. 30(24), 1515–1529 (2016).CrossRefGoogle Scholar
Khan, A. M., D.-w. Yun, M. A. Ali, K. M. Zuhaib, C. Yuan, J. Iqbal, J. Han, K. Shin and C. Han, “Passivity based adaptive control for upper extremity assist exoskeleton,” Int. J. Control Autom. Syst. 14(1), 291–300 (2016).CrossRefGoogle Scholar
Luna, C. O., Rahman, M. H., Saad, M., P. Archambault and W.-H. Zhu, “Virtual decomposition control of an exoskeleton robot arm,” Robotica 34(7), 1587–1609 (2016).CrossRefGoogle Scholar
Huang, A.-C. and Chien, M.-C., Adaptive Control of Robot Manipulators: a Unified Regressor-Free Approach (World Scientific, Singapore, 2010).Google Scholar
Roy, S. and Kar, I. N., Adaptive-Robust Control with Limited Knowledge on Systems Dynamics: An Artificial Input Delay Approach and Beyond, vol. 257 (Springer Nature, Singapore, 2019).Google Scholar
Brahmi, B., Saad, M., Luna, C. O., P. S. Archambault and M. H. Rahman, “Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties,” Robotica 36(11), 1757–1779 (2018).CrossRefGoogle Scholar
Ochoa Luna, C., Habibur Rahman, M., Saad, M., P. S. Archambault and S. B. Ferrer, “Admittance-based upper limb robotic active and active-assistive movements,” Int. J. Adv. Rob. Syst. 12(9), 117 (2015).CrossRefGoogle Scholar
Aviles, L. A. Z., Ortega, J. C. P. and Hurtado, E. G., “Experimental study of the methodology for the modelling and simulation of mobile manipulators,” Int. J. Adv. Rob. Syst. 9(5), 192 (2012).CrossRefGoogle Scholar
Brahmi, A., Saad, M., Gauthier, G., B. Brahmi, W-H. Zhu and J. Ghommam, “Adaptive Backstepping Control of Mobile Manipulator Robot Based on Virtual Decomposition Approach,” 2016 8th International Conference on Modelling, Identification and Control (ICMIC) (IEEE, 2016), pp. 707–712.CrossRefGoogle Scholar
Chen, W., Ge, S. S., Wu, J. and Gong, M., “Globally stable adaptive backstepping neural network control for uncertain strict-feedback systems with tracking accuracy known a priori,” IEEE Trans. Neural Networks Learn. Syst. 26(9), 1842–1854 (2015).Google Scholar
Li, Z., Su, C.-Y., Li, G. and Su, H., “Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs,” IEEE Trans. Fuzzy Syst. 23(3), 555–566 (2015).CrossRefGoogle Scholar
Chien, M.-C. and Huang, A.-C., “Adaptive impedance control of robot manipulators based on function approximation technique,” Robotica 22(4), 395–403 (2004).CrossRefGoogle Scholar
Levant, A., “Higher-order sliding modes, differentiation and output-feedback control,” Int. J. Control 76(9–10), 924–941 (2003).CrossRefGoogle Scholar
Suárez, M. B. and Heredia, R. R., “Kinematics, Dynamics and Evaluation of Energy Consumption for ABB IRB-140 Serial Robots in the Tracking of a Path,” The 2nd International Congress of Engineering Mechatron-ics and Automation (2013), pp. 23–25.Google Scholar
Craig, J. J., Introduction to Robotics: Mechanics and Control, vol. 3 (Pearson/Prentice Hall Upper Saddle River, NJ, USA, 2005).Google Scholar
Li, Z., Yang, C. and Fan, L., Advanced Control of Wheeled Inverted Pendulum Systems (Springer Science & Business Media, London, 2012).Google Scholar
Yazdani, M., Salarieh, H. and Foumani, M. S., “Bio-inspired decentralized architecture for walking of a 5-link biped robot with compliant knee joints,” Int. J. Control Autom. Syst. 16(6), 2935–2947 (2018).CrossRefGoogle Scholar