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Robust Adaptive Dynamic Surface Control of Multi-link Flexible Joint Manipulator with Input Saturation

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Abstract

This paper investigates the tracking control for multi-link flexible joint manipulator system with disturbance, uncertain stiffness and input saturation. A robust adaptive dynamic surface control scheme is developed with an auxiliary dynamic system. The auxiliary dynamic system is employed to handle input saturation. To compensate for disturbance, an adaptive law for disturbance upper bound estimation is designed. The value of uncertain stiffness is updated by another adaptive law. It is proved that the proposed control scheme can realize precise tracking of link angles and guarantee the uniform ultimate boundedness of all the signals in the closed-loop by appropriately choosing the parameters to be designed. Finally, simulation results demonstrate the effectiveness of the proposed control method.

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References

  1. W. He, Z. Yan, Y. Sun, Y. Ou, and C. Sun, “Neural-learning-based control for a constrained robotic manipulator with flexible joints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 12, pp. 5993–6003, 2018.

    Article  Google Scholar 

  2. A. De Luca, A. Albu-Schaffer, S. Haddadin, and G. Hirzinger, “Collision detection and safe reaction with the DLR-III lightweight manipulator arm,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, pp. 1623–1630, 2006.

  3. S. S. Ge, “Adaptive controller design for flexible joint manipulators,” Automatica, vol. 32, no. 2, pp. 273–278, 1996.

    Article  MathSciNet  Google Scholar 

  4. B. Brogliato, R. Ortega, and R. Lozano, “Global tracking controllers for flexible-joint manipulators: A comparative study,” Automatica, vol. 31, no. 7 pp. 941–956, 1995.

    Article  MathSciNet  Google Scholar 

  5. L. Huang, S. S. Ge and T. H. Lee, “Position/force control of uncertain constrained flexible joint robots,” Mechatronics, vol. 16, no. 2, pp. 111–120, 2006.

    Article  Google Scholar 

  6. M. Spong, K. Khorasani and P. Kokotovic, “An integral manifold approach to the feedback control of flexible joint robots,” IEEE Journal on Robotics and Automation, vol. 3, no. 4, pp. 291–300, 1987.

    Article  Google Scholar 

  7. R. Lozano and B. Brogliato, “Adaptive control of robot manipulators with flexible joints,” IEEE Transactions on Automatic Control, vol. 37, no. 2, pp. 174–181, 1992.

    Article  MathSciNet  Google Scholar 

  8. S. J. Yoo, J. B. Park, and Y. H. Choi, “Adaptive output feedback control of flexible-joint robots using neural networks: Dynamic surface design approach,” IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1712–1726, 2008.

    Article  Google Scholar 

  9. M. Ruderman and M. Iwasaki, “Sensorless torsion control of elastic-joint robots with hysteresis and friction,” IEEE Transactions on Industrial Electronics, vol. 63, no. 3, pp. 1889–1899, 2015.

    Article  Google Scholar 

  10. X. Liu, F. Zhao, S. S. Ge, Y. Wu, and X. Mei, “End-effector force estimation for flexible-joint robots with global friction approximation using neural networks,” IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1730–1741, 2018.

    Article  Google Scholar 

  11. Y. Wang, L. Gu, Y. Yu, and X. Cao, “Practical tracking control of robot manipulators with continuous fractional-Order nonsingular terminal sliding mode,” IEEE Transactions on Industrial Electronics, vol. 63, no. 10, pp. 6194–6204, 2016.

    Article  Google Scholar 

  12. Y. Wang, F. Yan, J. Chen, F. Ju, and B. Chen, “A new adaptive time-delay control scheme for cable-driven manipulators,” IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3469–3481, 2018.

    Article  Google Scholar 

  13. Y. Wang, L. Liu, D. Wang, F. Ju, and B. Chen, “Time-delay control using a novel nonlinear adaptive law for accurate trajectory tracking of cable-driven robots,” IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5234–5243, 2019.

    Article  Google Scholar 

  14. Y. Wang, S. Li, D. Wang, F. Ju, B. Chen, and H. Wu, “Adaptive time-delay control for cable-driven manipulators with enhanced nonsingular fast terminal sliding mode,” IEEE Transactions on Industrial Electronics, vol. 68, no. 3, pp. 2356–2367, 2021.

    Article  Google Scholar 

  15. J. Zhang, “State observer-based adaptive neural dynamic surface control for a class of uncertain nonlinear systems with input saturation using disturbance observer,” Neural Computing and Applications, vol. 31, pp. 4993–5004, 2019.

    Article  Google Scholar 

  16. C. C. Hua, J. N. Chen, and X. P. Guan, “Dynamic surface based tracking control of uncertain quadrotor unmanned aerial vehicles with multiple state variable constraints,” IET Control Theory and Applications, vol. 13, no. 4, pp. 526–533, 2019.

    Article  MathSciNet  Google Scholar 

  17. M. Z. Xia and T. P. Zhang, “Adaptive neural dynamic surface control for full state constrained stochastic nonlinear systems with unmodeled dynamics,” Journal of the Franklin Institute, vol. 356, no. 1, pp. 129–146, 2019.

    Article  MathSciNet  Google Scholar 

  18. G. H. Kim and K. S. Hong, “Adaptive sliding mode control of an offshore container crane with unknown disturbances,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 6, pp. 2850–2861, 2019.

    Article  Google Scholar 

  19. S. K. Park and S. H. Lee, “Disturbance observer based robust control for industrial robots with flexible joints,” Proc. of International Conference on Control Automation and Systems, Seoul, Korea, pp. 584–589, October 2007.

  20. S. E. Talole, J. P. Kolhe, and S. B. Phadke, “Extended-state-observer-based control of flexible-joint system with experimental validation,” IEEE Transactions on Industrial Electronics, vol. 57, no. 4, pp. 1411–1419, 2009.

    Article  Google Scholar 

  21. K. J. Kim and W. K. Chung, “Disturbance-observer-based PD control of flexible joint robots for asymptotic convergence,” IEEE Transactions on Robotics, vol. 31, no. 6, pp. 1508–1516, 2015.

    Article  Google Scholar 

  22. U. K. Sahu, B. Subudhi, and D. Patra, “Sampled-data extended state observer-based backstepping control of two-link flexible manipulator,” Transactions of the Institute of Measurement and Control, vol. 41, no. 13, pp. 3581–3599, 2019.

    Article  Google Scholar 

  23. M. Ruderman, T. Bertram, and M. Iwasaki, “Modeling, observation, and control of hysteresis torsion in elastic robot joints,” Mechatronics, vol. 57, no. 4, pp. 407–415, 2014.

    Article  Google Scholar 

  24. W. He, T. Meng, X. He, and S. S. Ge, “Unified iterative learning control for flexible structures with input constraints,” Automatica, vol. 96, pp. 326–336, 2018.

    Article  MathSciNet  Google Scholar 

  25. Y. Chen, D. S. Wang, C. C. Zhu, and S. J. Zhang, “Vibration Response Spectrum Analysis of Structures with Uncertain Stiffness under Random Base Exitation,” Journal of Vibration and Shock, vol. 23, no. 3, pp. 87–90, 2004.

    Google Scholar 

  26. G. H. Kim, “Continuous integral sliding mode control of an offshore container crane with input saturation,” International Journal of Control, Automation, and Systems, vol. 18, no. 9, pp. 2326–2336, 2020.

    Article  Google Scholar 

  27. Z. Liu, J. Liu, and W. He, “Partial differential equation boundary control of a flexible manipulator with input saturation,” International Journal of Systems Science, vol. 48, no. 1, pp. 53–62, 2017.

    Article  MathSciNet  Google Scholar 

  28. S. Ling, H. Wang and P. X. Liu, “Adaptive fuzzy dynamic surface control of flexible-joint robot systems with input saturation,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 1, pp. 97–107, 2019.

    Article  MathSciNet  Google Scholar 

  29. H. Liu, S. Zhu, J. Wu, and S. Zuo, “Trajectory tracking control for robot manipulators with bounded inputs based on the singular perturbation theory,” Control Theory and Applications, vol. 26, no. 12, pp. 1371–1377, 2009.

    Google Scholar 

  30. S. Ling, H. Q. Wang, P. X. Liu, “Adaptive fuzzy tracking control of flexible-joint robots based on command filtering,” IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 4046–4055, 2020.

    Article  Google Scholar 

  31. P. Ioannouand and J. Sun, Robust Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, 1996.

    Google Scholar 

  32. Z. Cai, M. S. de Queiroz, and D. M. Dawson, “A sufficiently smooth projection operator,” IEEE Transactions on Automatic Control, vol. 51, no. 1, pp. 135–139, 2006.

    Article  MathSciNet  Google Scholar 

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Funding

This work is supported by National Natural Science Foundation of China (61973167).

Author information

Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China

    Wei Yao, Yu Guo, Yi-Fei Wu & Jian Guo

Authors
  1. Wei Yao
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  2. Yu Guo
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  3. Yi-Fei Wu
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  4. Jian Guo
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Corresponding author

Correspondence to Yu Guo.

Additional information

Wei Yao received his B.S. degree in electrical information from Nanjing University of Science and Technology in 2014. Now he is a Ph.D. student in control science and engineering from Nanjing University of Science and Technology. His research interests include nonlinear control, adaptive control, vibration control, robot manipulator.

Yu Guo received her B.S. and M.S. degrees in automation from Huazhong University of Science and Technology, Wuhan, China, in 1984 and 1987, respectively, and a Ph.D. degree in control science and engineering from Nanjing University of Science and Technology. In 1987, she joined the faculty of the School of Automation, Nanjing University of Science and Technology, and is currently a Professor of Automatic Control there. Her main research interests include intelligent robot control, optimization for complicated systems and so forth.

Yi-Fei Wu received his Ph.D. degree in automation at Nanjing University of Science and Technology in 2014. He is currently a Professor in School of Automation, Nanjing University of Science and Technology. His research interests include servo system control, intelligent robots and integrated navigation.

Jian Guo received his Ph.D. degree in automation at Nanjing University of Science and Technology in 2002. He was a visiting scholar at Purdue University from 2008 to 2009 and is currently a Professor of Automatic Control at Nanjing University of Science and Technology. His research interests focus on robot system design and automatic control.

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Yao, W., Guo, Y., Wu, YF. et al. Robust Adaptive Dynamic Surface Control of Multi-link Flexible Joint Manipulator with Input Saturation. Int. J. Control Autom. Syst. 20, 577–588 (2022). https://doi.org/10.1007/s12555-020-0176-x

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  • DOI: https://doi.org/10.1007/s12555-020-0176-x

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