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Desired model compensation-based position constrained control of robotic manipulators

Published online by Cambridge University Press:  14 May 2021

Samet Gul ,
Erkan Zergeroglu Open the ORCID record for Erkan Zergeroglu [Opens in a new window] ,
Enver Tatlicioglu Open the ORCID record for Enver Tatlicioglu [Opens in a new window]  and
Mesih Veysi Kilinc
Samet Gul
Affiliation:
Department of Computer Engineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
Erkan Zergeroglu*
Affiliation:
Department of Computer Engineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
Enver Tatlicioglu
Affiliation:
Department of Electrical & Electronics Engineering, Ege University, 35100, Bornova, Izmir, Turkey
Mesih Veysi Kilinc
Affiliation:
Institute of Information Technologies, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
*
*Corresponding author. Email: [email protected]
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Abstract

This work presents the design and the corresponding stability analysis of a desired model-based, joint position constrained, controller formulation for robotic manipulators. Specifically, provided that the initial joint position tracking error signal starts within some predefined region, the proposed controller ensures that the joint tracking error signal remains inside this region and approaches to zero asymptotically. Extensive numerical simulations and experimental studies performed on a two-link direct-drive planar robot are provided in order to illustrate the effectiveness and feasibility of the proposed controller.

Keywords

Robotic manipulatorsPosition constraintFull-state feedbackLyapunov-based approach
Type
Article
Information
Robotica , Volume 40 , Issue 2 , February 2022 , pp. 279 - 293
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Merckaert, K., Vanderborght, B., Nicotra, M. and Garone, E., “Constrained Control of Robotic Manipulators Using the Explicit Reference Governor,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 5155–5162.Google Scholar
Ngo, K. B., Mahony, R. and Jiang, Z. P., “Integrator Backstepping Using Barrier Functions for Systems with Multiple State Constraints,” Proceedings of the 44th Conference on Decision and Control, Seville, Spain (2005) pp. 8306–8312.Google Scholar
Tee, K. P., Ge, S. S. and Tay, E. H., “Barrier lyapunov functions for the control of output-constrained nonlinear systems,” Automatica 45(4), 918–927 (2009).CrossRefGoogle Scholar
Tee, K. P., Ren, B. and Ge, S. S., “Control of nonlinear systems with time-varying output constraints,” Automatica 47(11), 2511–2516 (2011).CrossRefGoogle Scholar
Liu, Y. J. and Tong, S. C., “Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints,” Automatica 64, 70–75 (2016).CrossRefGoogle Scholar
Wang, C., Wu, Y. and Yu, J., “Barrier Lyapunov functions-based adaptive control for nonlinear pure-feedback systems with time-varying full state constraints,” Int. J. Cont. Automat. Syst. 15(6), 2714–2722 (2017).CrossRefGoogle Scholar
LAfflitto, A., “Barrier Lyapunov functions and constrained model reference adaptive control,” IEEE Cont. Syst. Lett. 2(3), 441–446 (2018).CrossRefGoogle Scholar
Buizza Avanzini, G., A. Zanchettin and P Rocco, “Constrained model predictive control for mobile robotic manipulators”, Robotica, 36, 19–38 (2018).CrossRefGoogle Scholar
Kabziński, J., Mosiołek, P. and Jastrzkbski, M., “Adaptive Position Tracking with Hard Constraints – Barrier Lyapunov Functions Approach,” In: Advanced Control of Electrical Drives and Power Electronic Converters (Kabziński, J., ed.) (Springer, Cham, Switzerland, 2017) pp. 27–52.CrossRefGoogle Scholar
Doulgeri, Z. and Zoidi, O., “Prescribed Performance Regulation for Robot manipulators,” Proceedings of International IFAC Symposium on Robot Control (2009) pp. 721–726.Google Scholar
Doulgeri, Z. and Karayiannidis, Y., “PID Type Robot Joint Position Regulation with Prescribed Performance Guaranties,” Proceedings of IEEE International Conference on Robotics and Automation (2010) pp. 4137–4142.Google Scholar
Karayiannidis, Y. and Doulgeri, Z., “Model-free robot joint position regulation and tracking with prescribed performance guarantees,” Robot. Autonom. Syst. 60, 214–226 (2012).CrossRefGoogle Scholar
Karayiannidis, Y. and Doulgeri, Z., “Regressor-free prescribed performance robot tracking,” Robotica 31, 1229–1238 (2013).CrossRefGoogle Scholar
Karayiannidis, Y., Papageorgiou, D. and Doulgeri, Z., “A model–free controller for guaranteed prescribed performance tracking of both robot joint positions and velocities,” IEEE Robot. Automat. Lett. 1(1), 267–273 (2016).CrossRefGoogle Scholar
Hackl, C. and Kennel, R., “Position Funnel Control for Rigid Revolute Joint Robotic Manipulators with Known Inertia Matrix,” Mediterranean Conference on Control and Automation (2012) pp. 615–620.Google Scholar
Doulgeri, Z. and Droukas, L., “Robot Task Space PID Type Regulation with Prescribed Performance Guaranties,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots Systems, Taipei, Taiwan (2010) pp. 1644–1649.Google Scholar
Papageorgiou, D., Atawnih, A. and Doulgeri, Z., “A Passivity Based Control Signal Guaranteeing Joint Limit Avoidance in Redundant Robots,” Mediterranean Conference on Control and Automation, Athens, Greece (2016) pp. 569–574.Google Scholar
Doulgeri, Z. and Karalis, P., “A Prescribed Performance Referential Control for Human-Like Reaching Movement of Redundant Arms,” Proceedings of International IFAC Symposium on Robot Control (2012) pp. 295–300.Google Scholar
Bechlioulis, C., Doulgeri, Z. and Rovithakis, G., “Guaranteeing prescribed performance and contact maintenance via an approximation free robot force/position controller,” Automatica 48(2), 360–365 (2012).CrossRefGoogle Scholar
Bechlioulis, C. P., Doulgeri, Z. and Rovithakis, G. A., “Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance,” IEEE Trans. Neural Netw. 21(12), 1857–1868 (2010).CrossRefGoogle ScholarPubMed
Ahanda, J. J. B. M., Mbede, J. B., Melingui, A. and Zob, B. E., “Robust adaptive command filtered control of a robotic manipulator with uncertain dynamic and joint space constraints,” Robotica 35, 767–786 (2018).CrossRefGoogle Scholar
Xia, J., Zhang, Y., Yang, C., Wang, M. and Annamalai, A., “An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions,” Int. J. Syst. Sci. 50(3), 638–651 (2019).CrossRefGoogle Scholar
Villalobos-Chin, J. and Santibáñez, V., “An adaptive regressor-free Fourier series-based tracking controller for robot manipulators: Theory and experimental evaluation”, Robotica, 1–16 (2021). doi: 10.1017/S0263574721000084.CrossRefGoogle Scholar
Khorashadizadeh, S. and Fateh, M. M., “Uncertainty estimation in robust tracking control of robot manipulators using the Fourier series expansion”, Robotica 35, 310–336 (2017).CrossRefGoogle Scholar
Lewis, F. L., Dawson, D. M. and Abdallah, C. T., Robot Manipulator Control: Theory and Practice (Marcel Dekker, Inc., New York, NY, USA, 2004).Google Scholar
Kokotovic, P., “The joy of feedback: Nonlinear and adaptive,” IEEE Cont. Syst. Mag. 12(3), 7–17 (1992).Google Scholar
Krstic, M., Kanellakopoulos, I. and Kokotovic, P. V., Nonlinear and Adaptive Control Design (Wiley, New York, NY, USA, 1995). ISBN: 988-0-471-12732-1.Google Scholar
Khalil, H. K., Nonlinear Systems, 3rd Edition (Prentice Hall, New York, NY, USA, 2002).Google Scholar
Direct Drive Manipulator Research and Development Package Operations Manual (Integrated Motion Inc., Berkeley, CA, 1992).Google Scholar