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Adaptive robust control based on RBF neural networks for duct cleaning robot

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  • Intelligent and Information Systems
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Abstract

In this paper, a control strategy for duct cleaning robot in the presence of uncertainties and various disturbances is proposed which combines the advantages of neural network technique and advanced adaptive robust theory. First of all, the configuration of the duct cleaning robot is introduced and the dynamic model is obtained based on the practical duct cleaning robot. Second, the RBF neural network is used to identify the unstructured and dynamic uncertainties due to its strong ability to approximate any nonlinear function to arbitrary accuracy. Using the learning ability of neural network, the designed controller can coordinately control the mobile plant and cleaning arm of duct cleaning robot with different dynamics efficiently. The neural network weights are only tuned on-line without tedious and lengthy off-line learning. Then, an adaptive robust control scheme based on RBF neural network is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. Finally, based on the Lyapunov stability theory, the stability of the whole closed-loop control system, and the uniformly ultimately boundedness of the tracking errors are all strictly guaranteed. Moreover, simulation and experiment results are given to demonstrate that the proposed control approach can guarantee the whole system converges to desired manifold with well performance.

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References

  1. V. Pavlov and A. Timofeyev, “Construction and stabilization of programmed movement of a mobile robot-manipulator,” Engineering Cybernetics, vol. 14, no. 6, pp. 70–79, 1976.

    Google Scholar 

  2. J. H. Chung and S. A. Venlinsky, “Modeling and control of a mobile manipulator,” Robotica, vol. 16, no. 6, pp. 607–613, May 1998.

    Article  Google Scholar 

  3. Y. Kim and E. Lewis, “Neural network output feedback control of robot manipulator,” IEEE Trans. Robot. Auto., vol. 15, no. 2, pp. 301–309, 1999.

    Article  Google Scholar 

  4. K. Watanabe, K. Sato, K. Izumi, and Y. Kunitake, “Analysis and control for an omnidirectional mobile manipulator,” J. Intell. Robot. System, vol. 27, no. 1, pp. 2–20, January 2000.

    Google Scholar 

  5. W. Dong, “On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty,” Automatica, vol. 38, no. 8, pp. 1475–1484, September 2002.

    Article  MATH  Google Scholar 

  6. Y. Liu and Y. Li, “Sliding mode adaptive neuralnetwork control for nonholonomic mobile modular manipulators,” J. Intell. Robot. System, vol. 44, no. 3, pp. 203–224, November 2005.

    Article  Google Scholar 

  7. Z. Li, S. S. Ge, M. Adams, and W. S. Wijesoma, “Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators,” Automatica, vol. 44, no. 1, pp. 776–784, 2008.

    Article  MathSciNet  Google Scholar 

  8. D. Xu, D. B. Zhao, J. Q. Yi, and X. M. Tan, “Robust adaptive control for omnidirectional mobile manipulators,” Proc. IEEE Int. Conf. Intell. Robots Syst., pp. 3598–3603, 2007.

    Google Scholar 

  9. R. Holmberg and O. Khabit, “Development and control of a holonomic mobile robot for mobile manipulation tasks,” Int. J. Robot. Res., vol. 19, no. 11, pp. 1066–1074, November 2000.

    Article  Google Scholar 

  10. Z. Li, P. Y. Tao, S. S. Ge, M. Adams, and W. S. Wijesoma, “Robust adaptive control of cooperating mobile manipulators with relative motion,” IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 39, no. 1, pp. 103–116, 2009.

    Article  MATH  Google Scholar 

  11. Z. Li, W. Chen, and H. Liu, “Robust control of wheeled mobile manipulators using hybrid joints,” International Journal of Advance Robotic Systems, vol. 5, no. 1, pp. 83–90, 2008.

    Google Scholar 

  12. Y. Yamamoto and X. Yun, “Effect of dynamic interaction on coordinate control of mobile manipulators,” IEEE Trans. on Robotics and Automation, vol. 12, no. 5, pp. 816–24, October 1996.

    Article  Google Scholar 

  13. T. G. Sugar and V. Kumar, “Control of cooperating mobile manipulators,” IEEE Trans. Robot. Auto., vol. 18, no. 1, pp. 94–103, 2004.

    Article  Google Scholar 

  14. O. Khabit, “Mobile manipulation: the robotic assistant,” Robotics and Autonomous Systems, vol. 26, no. 2–3, pp. 157–183, February 1999.

    Google Scholar 

  15. J. Tan, N. Xi, and Y. Wang, “Integrated task planning and control for mobile manipulators,” International Journal of Robotics Research, vol. 22, no. 5, pp. 337–354, May 2003.

    Article  Google Scholar 

  16. W. Khalil, “Dynamic modeling of robots using Newton-Euler formulation,” Informatics in Control, Automation and Robotics, LNEE89, pp. 3–20, 2011.

    Chapter  Google Scholar 

  17. A. A. Ata, “Dynamic modeling and numerical simulation of a nonholonomic mobile manipulator,” Int J. Mech. Mater, vol. 6, pp. 209–216, 2010.

    Article  Google Scholar 

  18. M. Boukattaya, T. Damak, M. Jallouli, “Adaptive robust tracking control for mobile manipulators in the task-space under uncertainties,” International Journal of Intelligent Computing and Cybernetics, vol. 4, no. 1, pp. 81–92, February 2011.

    Article  MATH  MathSciNet  Google Scholar 

  19. Z. Li, W. Chen, and J. Luo, “Adaptive compliant force-motion control of coordinate nonholonomic mobile manipulators interacting with unknown non-rigid environment,” Neuro computing, vol. 7, no. 10, pp. 1330–1344, March 2008.

    Google Scholar 

  20. M. Boukattaya, T. Damak, and M. Jallouli, “Robust adaptive control for mobile manipulators,” International Journal of Automation and Computing, vol. 8, no. 10, pp. 8–13, February 2011.

    Article  Google Scholar 

  21. S. Lin and A. Goldenberg, “Robust damping control of mobile manipulators,” IEEE Trans. Sys. Man. Cyber., vol. 32, no. 1, pp. 126–132, 2002.

    Article  Google Scholar 

  22. J.-C. Ryu and S. K. Argawal, “Planning and control of under-actuated mobile manipulators using differential flatness,” Autonomous Robots, vol. 29, no. 1, pp. 35–52, April 2010.

    Article  Google Scholar 

  23. M. Zeinali and L. Notash, “Adaptive sliding mode control with uncertainty estimator for robot manipulators,” Mechanism and Machine Theory, vol. 45, no. 1, pp. 80–90, January 2010.

    Article  MATH  MathSciNet  Google Scholar 

  24. Y. N. Wang, Wei Sun, Y. Q. Xiang, and S. Y. Miao, “Neural network-based robust tracking control for robots,” Intelligent Automation and Soft Computing, vol. 15, no. 2, pp. 211–222, June 2009.

    Article  Google Scholar 

  25. J. Jang, “Adaptive neuro-fuzzy network control for a mobile robot,” J. Intell. Robot. Syst., vol. 62, no. 3–4, pp. 567–586, June 2011.

    Article  MATH  Google Scholar 

  26. S. Lin and A. Goldenberg, “Neural-network control of mobile manipulators,” IEEE Trans. Neural networks, vol. 12, no. 5, pp. 1121–1133, 2001.

    Article  Google Scholar 

  27. Y. Zuo, Y. Wang, X. Liu, S. X. Yang, H. Lihong, X. Wu, and W. Zengyun, “Neural network robust H 8 tracking control strategy for robotic manipulators,” Applied Mathematical Modeling, vol. 34, no. 7, pp. 1823–1838, July 2010.

    Article  MATH  Google Scholar 

  28. X. Wu, Y. Wang, H. Lihong, and Y. Zuo, “Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays,” Neurocomputing, vol. 73, no. 7–9, pp. 1265–1271, March 2010.

    Article  Google Scholar 

  29. J. Peng, Y. Wang, and H. Yu, “Neural networkbased robust tracking control for nonholonomic mobile robot,” Advances in Neural Networks, ISNN2007, LNCS, vol. 4491, Part I, pp. 804–812, 2007.

    Google Scholar 

  30. D. Xu, D. B. Zhao, J. Q. Yi, and X. M. Tan, “Trajectory tracking control of omnidirectional wheeled mobile manipulators: robust neural network-based sliding mode approach,” IEEE Trans. on systems, man, Cybernetics-part B: cybernetics, vol. 39, no. 9, pp. 788–799, June 2009.

    Google Scholar 

  31. A. Rojko and K. Jezernik, “Sliding mode motion controller with adaptive fuzzy disturbance estimation,” IEEE Trans. Industrial Electronic, vol. 51, no. 5, pp. 963–971, October 2004.

    Article  Google Scholar 

  32. C.-Y. Chen, T.-H. S. Li, Y.-C. Yeh, and C.-C. Chang, “Design and implementation of an adaptive sliding-mode dynamic controller for wheel mobile robots,” Mechatronics, vol. 19, no. 2, pp. 156–166, March 2009.

    Article  Google Scholar 

  33. Z. Li, Y. Yang, and S. Wang, “Adaptive dynamic coupling control of hybrid joints of humansymbiotic wheeled mobile manipulators with unmodelled dynamics,” Int J. Soc Robot, vol. 2, pp. 109–120, March 2010.

    Article  Google Scholar 

  34. C.-L. Hwang, L.-J. Chang, and Y.-S. Yu, “Network-based fuzzy decentralized sliding-mode control for car-like Mobile robots,” IEEE Trans. on Industrial Electronics, vol. 54, no. 1, pp. 574–585, February 2007.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan, 410082, China

    Bu Dexu & Sun Wei

  2. College of Electric and Information Engineering, Hunan University, Changsha, Hunan, 410082, China

    Yu Hongshan & Wang Cong

  3. College of Electric and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410004, China

    Zhang Hui

Authors
  1. Bu Dexu
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  2. Sun Wei
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  3. Yu Hongshan
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  4. Wang Cong
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  5. Zhang Hui
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Corresponding authors

Correspondence to Sun Wei or Yu Hongshan.

Additional information

Bu Dexu was born in 1986, he is a Ph.D. candidate in the College of Electrical and Information Engineering, Hunan University now. His research interests include intelligent control, machine vision, robotic and robust control.

Sun Wei received his B.S, M.S, and Ph.D. degrees from the Department of Automation Engineering, Hunan University, P. R. China, in 1997, 1999 and 2002, respectively. He now is working as a Professor at the College of Electrical and Information Engineering, Hunan University. His areas of interests are neural networks, intelligent control.

Yu Hongshan is an associate Professor, College of Electrical and Information Engineering, Hunan University. He received his B.S., M.S., and Ph.D. degrees from Hunan University in 2001, 2004 and 2007. His research interests include mobile robots navigation.

Wang Cong received his Master degree in Control Science and Engineering from Hunan University in 2010. He is a Ph.D. candidate at the College of Electrical and Information Engineering, Hunan University. His research interests include robotics system control

Zhang Hui was born in 1983, assistant professor, College of Electrical and Information Engineering, Changsha University of Science and Technology. He received his Ph.D. degree from Hunan University in 2012. His research interests include robotic control.

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Dexu, B., Wei, S., Hongshan, Y. et al. Adaptive robust control based on RBF neural networks for duct cleaning robot. Int. J. Control Autom. Syst. 13, 475–487 (2015). https://doi.org/10.1007/s12555-012-0447-9

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  • DOI: https://doi.org/10.1007/s12555-012-0447-9

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