Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T03:08:45.027Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive impedance control of uncertain robot manipulators with saturation effect based on dynamic surface technique and self-recurrent wavelet neural networks

Published online by Cambridge University Press:  05 October 2018

Mohammad Hossein Hamedani ,
Maryam Zekri  and
Farid Sheikholeslam
Mohammad Hossein Hamedani
Affiliation:
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. E-mails: mh.hamedani@ec.iut.ac.ir, sheikh@cc.iut.ac.ir
Maryam Zekri*
Affiliation:
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. E-mails: mh.hamedani@ec.iut.ac.ir, sheikh@cc.iut.ac.ir
Farid Sheikholeslam
Affiliation:
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. E-mails: mh.hamedani@ec.iut.ac.ir, sheikh@cc.iut.ac.ir
*
*Corresponding author. E-mail: mzekri@cc.iut.ac.ir
Get access Rights & Permissions [Opens in a new window]

Summary

Saturation nonlinearities, among the known challenges in control engineering, are ubiquitous in robotic systems and can lead to stability and performance degradation. In this paper, an adaptive dynamic surface impedance (ADSI) control approach is developed for an n-link robotic manipulator by employing self-recurrent wavelet neural networks (SRWNNs) in order to overcome the saturation effect. The proposed control approach is inspired by the theory of dynamic surface control (DSC) and SRWNNs. As a novel application of the dynamic surface method to obtain a simple structure, the target impedance is formulated in the state–space, and effective dynamic surfaces are defined to track the desired impedance behavior. In fact, DSC is used to force the robot manipulator to track the desired impedance, while the robot interacts with an environment. In addition, SRWNNs are applied to approximate the parametric uncertainties and external disturbances in the robot dynamical model. Self-feedback neurons are embedded as memory units in SRWNNs to model the sudden dynamic jumps of the environment. Using Lyapunov's method, an ADSI controller is designed, and adaptation laws are induced to guarantee the stability of the closed-loop system. Finally, simulations are conducted to verify the proper performance of the proposed approach for removing the saturation effect and tracking the target impedance. It is worth noting that the simulation results indicate the robustness of the controller against uncertainties and external disturbances.

Keywords

Adaptive dynamic surface controlImpedance controlInput saturationRecurrent wavelet neural network
Type
Articles
Information
Robotica , Volume 37 , Issue 1 , January 2019 , pp. 161 - 188
Copyright
Copyright © Cambridge University Press 2018 

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

1. He, W., Dong, Y. and Sun, C., “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Trans. Syst., Man, Cybern., Syst. 46 (3), 334–344 (2016).Google Scholar
2. Hogan, N., “Impedance control: An approach to manipulation. Part I: Theory; Part II: Implementation; Part III: Applications,” J. Dyn. Syst. Meas. Control 107 (1), 8–16 (1985).Google Scholar
3. Chan, S. P., Yao, B., Gao, W. B. and Cheng, M., “Robust Impedance control of robot manipulators,” Int. J. Robot. Autom. 6 (4), 220–227 (1991).Google Scholar
4. Canudas de Wit, C., Siciliano, B. and Bastin, G. (Eds), Theory of Robot Control (Springer-Verlag, London, 1996), ISBN-13:978-1-4471-1503-8.Google Scholar
5. Mvogo Ahanda, J. J.-B., Mbede, J. B., Melingui, A. and Essimbi, B., “Robust adaptive control for robot manipulators: Support vector regression-based command filtered adaptive backstepping approach,” Robotica 36, 516–532 (2018).Google Scholar
6. Oh, J. H. and Lee, J. S., “Control of Flexible Joint Robot System by Backstepping Design Approach,” Proceedings of the IEEE International Conference on Robotics and Automation (1997) pp. 3435–3440.Google Scholar
7. Tong, S.-C. and Li, Y.-M., “Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown deadzones,” IEEE Trans. Fuzzy Syst. 20 (1), 168–180 (2012).Google Scholar
8. Kwan, C. and Lewis, F. L., “Robust backstepping control of nonlinear systems using neural networks,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans 30 (6), 753–766 (Nov. 2000).Google Scholar
9. Swaroop, D., Hedrick, J. K., Yip, P. P. and Gerdes, J. C., “Dynamic surface control for a class of nonlinear systems,” IEEE Trans. Autom. Control 45 (10), 1893–1899 (2000).Google Scholar
10. Yip, P. P. and Hedrick, J. K., “Adaptive dynamic surface control: A simplified algorithm for adaptive backstepping control of nonlinear systems,” Int. J. Control 71 (5), 959–979 (1998).Google Scholar
11. Wang, D. and Huang, J., “Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form,” IEEE Trans. Neural Netw. 16 (1), 195–202 (2005).Google Scholar
12. Kim, Y. H. and Lewis, F. L., “Reinforcement adaptive learning neural-net based friction compensation control for high speed and precision,” IEEE Trans. Control Syst. Technol. 8 (1), 118–126 (2000).Google Scholar
13. Xu, B., “Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity,” Nonlin. Dyn. 80 (3), 1509–1520 (2015).Google Scholar
14. Chien, M.-C. and Huang, A.-C., “Adaptive impedance control of robust manipulators based on function approximation technique,” Robotica 22 (4), 395–403 (2004).Google Scholar
15. Kamnik, R., Matko, D. and Bajd, T., “Application of model reference adaptive control to industrial robot impedance control,” J. Intell. Robot. Syst. 22 (2), 153–163 (1998).Google Scholar
16. Cheah, C. and Wang, D.-W., “Learning impedance control for robotic manipulators,” IEEE Trans. Robot. Autom. 14 (3), 452–465 (1998).Google Scholar
17. Lu, W.-S. and Meng, Q.-H., “Impedance control with adaptation for robotic manipulations,” IEEE Trans. Robot. Autom. 7 (3), 408–415 (1991).Google Scholar
18. Colbaugh, R., Seraji, H. and Glass, K., “Direct adaptive impedance control of robot manipulators,” J. Robot. Syst. 10 (2), 217–248 (1993).Google Scholar
19. Yoo, S. J., Park, J. B. and Choi, Y. H., “Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks,” IEEE Trans. Syst., Man, Cybern., Syst. 36 (6), 1342–1355 (2006).Google Scholar
20. Gaeta, M., Loia, V., Orciuoli, F. and Ritrovato, P., “S-WOLF: Semantic workplace learning framework,” IEEE Trans. Syst., Man, Cybern., Syst. 45 (1), 56–72 (2015).Google Scholar
21. Li, Z. and Su, C.-Y., “Neural-adaptive control of single-master–multiple slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties,” IEEE Trans. Neural Netw. Learn. Syst., 24 (9), 1400–1413 (2013).Google Scholar
22. Dai, S.-L., Wang, C. and Luo, F., “Identification and learning control of ocean surface ship using neural networks,” IEEE Trans. Ind. Inform. 8 (4), 801–810 (2012).Google Scholar
23. Liu, Z., Chen, C., Zhang, Y. and Chen, C. L. P., “Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism,” IEEE Trans. Cybern. 45 (3), 521–532 (2015).Google Scholar
24. Hsu, C. F., Lin, C. M. and Yeh, R. G., “Supervisory adaptive dynamic RBF-based neural fuzzy control system design for unknown nonlinear systems,” Appl. Soft Comput. 13, 1620–1626 (2013).Google Scholar
25. Rong, H. J., Han, S. and Zhao, G. S., “Adaptive fuzzy control of aircraft wing-rock motion,” Appl. Soft Comput. 14, 181–193 (2013).Google Scholar
26. Mohamad Dehghan, S. A., Danesh, M., Sheikholeslam, F. and Zekri, M., “Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network,” Appl. Soft Comput. 28, 527–540 (2015).Google Scholar
27. Zekri, M., Sadri, S. and Sheikholeslam, F., “Adaptive fuzzy wavelet network control design for nonlinear systems,” Fuzzy Sets Syst. 159, 2668–2695 (2008).Google Scholar
28. Zhang, Q. and Benveniste, A., “Wavelet networks,” IEEE Trans. Neural Netw. 3 (6), 889–898 (1992).Google Scholar
29. Zhang, J., Walter, G., Miao, Y. and Lee, W. N. W., “Wavelet neural networks for function learning,” IEEE Trans. Signal Process. 43 (6), 1485–1497 (1995).Google Scholar
30. Oussar, Y., Rivals, I., Personnaz, L. and Dreyfus, G., “Training wavelet networks for nonlinear dynamic input–output modeling,” Neurocomputing 20 (1–3), 173–188 (1998).Google Scholar
31. Bakshi, B. R. and Stephanopoulos, G., “Wave-net: A multiresolution, hierarchical neural network with localized learning,” AIChE J. 39 (1), 57–81 (1993).Google Scholar
32. Rioul, O. and Vetterli, M., “Wavelets and signal processing,” IEEE Signal Process. Mag. 8 (4), 14–38 (1991).Google Scholar
33. Lin, F. J., Shieh, H. J., Shieh, P. H. and Shen, P. H., “An adaptive recurrent neural-network motion controller for X–Y table in CNC machine,” IEEE Trans. Syst., Man, Cybern. B, Cybern. 36 (2), 286–299 (2006).Google Scholar
34. Lin, C. M. and Hsu, C. F., “Recurrent-neural-network-based adaptive backstepping control for induction servomotors,” IEEE Trans. Ind. Electron. 52 (6), 1677–1684 (2005).Google Scholar
35. Yoo, S. J., Choi, Y. H. and Park, J. B., “Identification of dynamic systems using a self-recurrent wavelet neural network: Convergence analysis via adaptive learning rates,” J. Control Autom. Syst. 11 (9), 781–788 (2005).Google Scholar
36. Yoo, S. J., Park, J. B. and Choi, Y. H., “Stable predictive control of chaotic systems using self-recurrent wavelet neural network,” Int. J. Control Autom. Syst. 3 (1), 43–55 (2005).Google Scholar
37. Santibañez, V., Camarillo, K., Moreno-Valenzuela, J. and Campa, R., “A practical PID regulator with bounded torques for robot manipulators,” Int. J. Control Autom. Syst. 8 (3), 544–555 (2010).Google Scholar
38. Huang, J., Wen, C., Wang, W. and Jiang, Z.-P., “Adaptive stabilization and tracking control of a nonholonomic mobile robot with input saturation and disturbance,” Syst. Control Lett. 62 (3), 234–241 (2013).Google Scholar
39. Chen, M., Jiang, B., Zou, J. and Feng, X., “Robust adaptive tracking control of the underwater robot with input nonlinearity using neural networks,” Int. J. Comput. Intell. Syst. 3 (5), 646–-655 (2010).Google Scholar
40. Li, Y., Ge, S. S. and Yang, C., “Learning impedance control for physical robot–environment interaction,” Int. J. Control 85 (2), 182–193 (2012).Google Scholar