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

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• 作者：Bu Dexu (1)
  Sun Wei (1)
  Yu Hongshan (2)
  Wang Cong (2)
  Zhang Hui (3)
  
  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body ; Hunan University ; Changsha ; Hunan ; 410082 ; China
  2. College of Electric and Information Engineering ; Hunan University ; Changsha ; Hunan ; 410082 ; China
  3. College of Electric and Information Engineering ; Changsha University of Science and Technology ; Changsha ; Hunan ; 410004 ; China
  
• 关键词：Adaptive robust control ; duct cleaning robot ; Lyapunov stability theory ; RBF neural network ; uncertainties
• 刊名：International Journal of Control, Automation and Systems
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：13
• 期：2
• 页码：475-487
• 全文大小：1,461 KB
• 参考文献：1. Pavlov, V., Timofeyev, A. (1976) Construction and stabilization of programmed movement of a mobile robot-manipulator. Engineering Cybernetics 14: pp. 70-79
  2. Chung, J. H., Venlinsky, S. A. (1998) Modeling and control of a mobile manipulator. Robotica 16: pp. 607-613 CrossRef
  3. Kim, Y., Lewis, E. (1999) Neural network output feedback control of robot manipulator. IEEE Trans. Robot. Auto. 15: pp. 301-309 CrossRef
  4. Watanabe, K., Sato, K., Izumi, K., Kunitake, Y. (2000) Analysis and control for an omnidirectional mobile manipulator. J. Intell. Robot. System 27: pp. 2-20
  5. Dong, W. (2002) On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty. Automatica 38: pp. 1475-1484 CrossRef
  6. Liu, Y., Li, Y. (2005) Sliding mode adaptive neuralnetwork control for nonholonomic mobile modular manipulators. J. Intell. Robot. System 44: pp. 203-224 CrossRef
  7. Li, Z., Ge, S. S., Adams, M., Wijesoma, W. S. (2008) Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators. Automatica 44: pp. 776-784 CrossRef
  8. Xu, D., Zhao, D. B., Yi, J. Q., Tan, X. M. (2007) Robust adaptive control for omnidirectional mobile manipulators. Proc. IEEE Int. Conf. Intell. Robots Syst.. pp. 3598-3603
  9. Holmberg, R., Khabit, O. (2000) Development and control of a holonomic mobile robot for mobile manipulation tasks. Int. J. Robot. Res. 19: pp. 1066-1074 CrossRef
  10. Li, Z., Tao, P. Y., Ge, S. S., Adams, M., Wijesoma, W. S. (2009) Robust adaptive control of cooperating mobile manipulators with relative motion. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 39: pp. 103-116 CrossRef
  11. Li, Z., Chen, W., Liu, H. (2008) Robust control of wheeled mobile manipulators using hybrid joints. International Journal of Advance Robotic Systems 5: pp. 83-90
  12. Yamamoto, Y., Yun, X. (1996) Effect of dynamic interaction on coordinate control of mobile manipulators. IEEE Trans. on Robotics and Automation 12: pp. 816-24 CrossRef
  13. Sugar, T. G., Kumar, V. (2004) Control of cooperating mobile manipulators. IEEE Trans. Robot. Auto. 18: pp. 94-103 CrossRef
  14. Khabit, O. (1999) Mobile manipulation: the robotic assistant. Robotics and Autonomous Systems 26: pp. 157-183
  15. Tan, J., Xi, N., Wang, Y. (2003) Integrated task planning and control for mobile manipulators. International Journal of Robotics Research 22: pp. 337-354 CrossRef
  16. Khalil, W. (2011) Dynamic modeling of robots using Newton-Euler formulation. Informatics in Control, Automation and Robotics, LNEE89. pp. 3-20 CrossRef
  17. Ata, A. A. (2010) Dynamic modeling and numerical simulation of a nonholonomic mobile manipulator. Int J. Mech. Mater 6: pp. 209-216 CrossRef
  18. Boukattaya, M., Damak, T., Jallouli, M. (2011) Adaptive robust tracking control for mobile manipulators in the task-space under uncertainties. International Journal of Intelligent Computing and Cybernetics 4: pp. 81-92 CrossRef
  19. Li, Z., Chen, W., Luo, J. (2008) Adaptive compliant force-motion control of coordinate nonholonomic mobile manipulators interacting with unknown non-rigid environment. Neuro computing 7: pp. 1330-1344
  20. Boukattaya, M., Damak, T., Jallouli, M. (2011) Robust adaptive control for mobile manipulators. International Journal of Automation and Computing 8: pp. 8-13 CrossRef
  21. Lin, S., Goldenberg, A. (2002) Robust damping control of mobile manipulators. IEEE Trans. Sys. Man. Cyber. 32: pp. 126-132 CrossRef
  22. Ryu, J.-C., Argawal, S. K. (2010) Planning and control of under-actuated mobile manipulators using differential flatness. Autonomous Robots 29: pp. 35-52 CrossRef
  23. Zeinali, M., Notash, L. (2010) Adaptive sliding mode control with uncertainty estimator for robot manipulators. Mechanism and Machine Theory 45: pp. 80-90 CrossRef
  24. Wang, Y. N., Sun, W., Xiang, Y. Q., Miao, S. Y. (2009) Neural network-based robust tracking control for robots. Intelligent Automation and Soft Computing 15: pp. 211-222 CrossRef
  25. Jang, J. (2011) Adaptive neuro-fuzzy network control for a mobile robot. J. Intell. Robot. Syst. 62: pp. 567-586 CrossRef
  26. Lin, S., Goldenberg, A. (2001) Neural-network control of mobile manipulators. IEEE Trans. Neural networks 12: pp. 1121-1133 CrossRef
  27. Zuo, Y., Wang, Y., Liu, X., Yang, S. X., Lihong, H., Wu, X., Zengyun, W. (2010) Neural network robust H 8 tracking control strategy for robotic manipulators. Applied Mathematical Modeling 34: pp. 1823-1838 CrossRef
  28. Wu, X., Wang, Y., Lihong, H., Zuo, Y. (2010) Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays. Neurocomputing 73: pp. 1265-1271 CrossRef
  29. Peng, J., Wang, Y., Yu, H. (2007) Neural networkbased robust tracking control for nonholonomic mobile robot. Advances in Neural Networks, ISNN2007, LNCS 4491: pp. 804-812
  30. Xu, D., Zhao, D. B., Yi, J. Q., Tan, X. M. (2009) Trajectory tracking control of omnidirectional wheeled mobile manipulators: robust neural network-based sliding mode approach. IEEE Trans. on systems, man, Cybernetics-part B: cybernetics 39: pp. 788-799
  31. Rojko, A., Jezernik, K. (2004) Sliding mode motion controller with adaptive fuzzy disturbance estimation. IEEE Trans. Industrial Electronic 51: pp. 963-971 CrossRef
  32. Chen, C.-Y., Li, T.-H. S., Yeh, Y.-C., Chang, C.-C. (2009) Design and implementation of an adaptive sliding-mode dynamic controller for wheel mobile robots. Mechatronics 19: pp. 156-166 CrossRef
  33. Li, Z., Yang, Y., Wang, S. (2010) Adaptive dynamic coupling control of hybrid joints of humansymbiotic wheeled mobile manipulators with unmodelled dynamics. Int J. Soc Robot 2: pp. 109-120 CrossRef
  34. Hwang, C.-L., Chang, L.-J., Yu, Y.-S. (2007) Network-based fuzzy decentralized sliding-mode control for car-like Mobile robots. IEEE Trans. on Industrial Electronics 54: pp. 574-585 CrossRef
  
• 刊物类别：Engineering
• 刊物主题：Control Engineering
  
• 出版者：The Institute of Control, Robotics and Systems Engineers and The Korean Institute of Electrical Engi
• ISSN：2005-4092

文摘

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.