Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle

IEEE Transactions on Robotics and Automation

Volumn 19, Issue 2, 2003, Pages 283-295

  Wang, Jeen Shing ^a,b   Lee, C S George ^a  

^a PURDUE UNIVERSITY   (United States)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Autonomous underwater vehicles; Inverse dynamics modeling Recurrent neuro fuzzy systems; Recursive recurrent learning algorithm

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ALGORITHMS; ERROR ANALYSIS; FEEDBACK CONTROL; FUZZY CONTROL; RECURRENT NEURAL NETWORKS; RECURSIVE FUNCTIONS;

NEURO-FUZZY CONTROL;

REMOTELY OPERATED VEHICLES;

EID: 0037387654     PISSN: 1042296X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TRA.2003.808865     Document Type: Article

References (32)

1. D. Wit, C. Canudas, D. O. Olguin, and M. Perrier, "Nonlinear control of an underwater vehicle/manipulator with composite dynamics," IEEE Trans. Contr. Syst. Technol., vol. 8, pp. 948-960, Nov. 2000.
2. T. I. Fossen, Guidance and Control of Ocean Vehicles. New York: Wiley, 1994.
3. T. I. Fossen and M. Blanke, "Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity," IEEE J. Oceanic Eng., vol. 25, pp. 241-255, Apr. 2000.
4. A. J. Healey and D. Lienard, "Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles," IEEE J. Oceanic Eng., vol. 18, pp. 327-339, July 1993.
5. D. N. Yoerger and J. E. Slotine, "Robust trajectory control of underwater vehicles," IEEE J. Oceanic Eng., vol. OE-10, pp. 462-470, Sept. 1985.
6. S. K. Choi and J. Yuh, "Experimental study on a learning control system with bound estimation for underwater vehicles," in Underwater Robots, J. Yuh, T. Ura, and G. A. Bekey, Eds. Norwell, MA: Kluwer, 1996, pp. 113-120.
7. K. Ishii, T. Fujii, and T. Ura, "An on-line adaptation method in a neural network-based control system for AUVs," IEEE J. Oceanic Eng., vol. 20, pp. 221-228, July 1995.
8. W. Lee and G. Kang, "A fuzzy model-based controller of an underwater robotic vehicle under the influence of thruster dynamics," in Proc. IEEE Int. Conf. Robotics and Automation, Leuven, Belgium, May 1998, pp. 750-755.
9. K. P. Venugopal, A. S. Pandya, and R. Sudhakar, "A recurrent neural network controller and learning algorithm for the on-line learning control of autonomous underwater vehicles," Neural Networks, vol. 7, no. 5, pp. 833-846, 1994.
10. J. S. Wang, C. S. G. Lee, and J. Yuh, "An on-line self-organizing neurofuzzy control for autonomous underwater vehicles," in Proc. IEEE Int. Conf. Robotics and Automation, Detroit, MI, May 1999, pp. 2416-2421.
11. J. Yuh, "Modeling and control of underwater robotic vehicles," IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 1475-1483, June 1990.
12. J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Boston, MA: Kluwer, 1999.
13. S. K. Choi, "An adaptive-learning control system for underwater robotic vehicles," Ph.D. dissertation, Dept. Mech. Eng., Univ. of Hawaii, 1995.
14. C. S. G. Lee and J. S. Wang, "Self-adaptive neuro-fuzzy systems: Structure and learning," in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Takamatsu, Japan, 2000, pp. 52-57.
15. C. S. G. Lee, J. S. Wang, and J. Yuh, "Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control," Adv. Robot., vol. 15, no. 5, pp. 589-608, 2001.
16. H. Miyamoto, M. Kawato, T. Setoyama, and R. Suzuki, "Feedback-error-learning neural networks for trajectory control of a robotic manipulator," Neural Networks, vol. 11, pp. 251-265, 1988.
17. J. Nie, "Adaptive control of autonomous underwater vehicles: simulation and experiment," Master's thesis, Dept. Mech. Eng., Univ. of Hawaii, 1998.
18. R. E. Nordgren and P. H. Meckl, "An analytical comparison of a neural network and a model-based adaptive controller," IEEE Trans. Neural Networks, vol. 4, pp. 685-694, Apr. 1993.
19. M. Sugeno, "On stability of fuzzy systems expressed by fuzzy rules with singleton consequents," IEEE Trans. Fuzzy Syst., vol. 7, pp. 201-224, Apr. 1999.
20. J. S. Wang and C. S. G. Lee, "Efficient neuro-fuzzy control systems for autonomous underwater vehicles," in Proc. IEEE Int. Conf. Robotics and Automation, Seoul, Korea, May 2001, pp. 2986-2991.
21. L. X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," IEEE Trans. Neural Networks, vol. 3, pp. 807-814, May 1992.
22. P. Werbos, "Beyond regression: new tools for prediction and analysis in the behavior sciences," Ph.D. dissertation, Harvard Univ., Cambridge, MA, 1974.
23. R. J. William and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks," Neural Computation, vol. 1, pp. 270-280, 1989.
24. J. Yuh and M. West, "Underwater robotics," Adv. Robot., vol. 15, no. 5, pp. 609-639, 2001.
25. D. R. Yoerger, J. G. Cooke, and J.-J. Slotine, "The influence of thruster dynamics on underwater vehicle behavior and their incorporation into control system design," IEEE J. Oceanic Eng., vol. 15, pp. 167-178, 1990.
26. A. J. Healey, S. M. Rock, S. Cody, D. Miles, and J. P. Brown, "Toward an improved understanding of thruster dynamics for underwater vehicles," IEEE J. Oceanic Eng., vol. 20, pp. 354-361, 1995.
27. J. B. McQueen, "Some methods of classification and analysis of multi-variate observations," in Proc. 5th Berkeley Symp. Math. Statist. Probability, 1967, pp. 281-297.
28. G. A. Carpenter, S. Grossberg, and D. B. Rosen, "Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system," Neural Networks, vol. 4, pp. 759-771, 1991.
29. C. F. Juang and C. T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications." IEEE Trans. Fuzzy Syst., vol. 6, pp. 12-32, Jan. 1998.
30. B. A. Pearlmutter, "Gradient calculations for dynamic recurrent neural networks: A survey," IEEE Trans. Neural Networks, vol. 6, pp. 1212-1228, May 1995.
31. J.-S. Wang, "On the structure and learning of self-adaptive neuro-fuzzy inference systems," Ph.D. dissertation, School Elect. Comput. Eng., Purdue Univ., West Lafayette, IN, 2001.
32. J.-S. Wang and C. S. G. Lee, "Self-adaptive neural-fuzzy inference systems for classification applications," IEEE Trans. Fuzzy Systems, vol. 10, pp. 790-802, Dec. 2002.