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신경회로망을 이용한 자율무인잠수정의 적응제어

Adaptive Neural Network Control for an Autonomous Underwater Vehicle

  • 발행 : 2002.12.01

초록

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

키워드

참고문헌

  1. S. K. Choi and J. Yuh, 'Experimental study on a learning control system with bound estimation for underwater robots,' Int. Journal of Autonomous Robots, Vol. 3, pp. 187-194, 1996 https://doi.org/10.1007/BF00141154
  2. G. Cybenko, 'Approximation by superpositions of a sigmoidal function,' Mathematics of Control, Signals and Systems, Vol. 2, pp. 303-314, 1989 https://doi.org/10.1007/BF02551274
  3. K. Hornik, M. Stinchcombe, and H. White, 'Multilayer feedforward networks are universal approximators,' Neural Networks, Vol. 2, pp. 359-366, 1989 https://doi.org/10.1016/0893-6080(89)90020-8
  4. K. I. Funahashi, 'On the approximate realization of continous mappings by neural networks,' Neural Networks, Vol. 2, pp. 183-192, 1989 https://doi.org/10.1016/0893-6080(89)90003-8
  5. F. Girosi and T. Poggio, 'Networks and the best approximation property,' Biological Cybernetics, 63, pp. 169-176, 1990 https://doi.org/10.1007/BF00195855
  6. K. P. Venugopal, P. Sudhakar, and A. S. Pandya, 'On-line learnig control of autonomous underwater vehicle using feedforward neural networks,' IEEE Journal of Oceanic Engineering, Vol. 17, No. 4, pp. 308-319, October 1992 https://doi.org/10.1109/48.180299
  7. Yuh, J., 'Learning control for underwater robotic vehicles,' IEEE Control Systems Magazine, pp. 39-46, 1994 https://doi.org/10.1109/37.272779
  8. K. Ishii, T. Fujii, and T. Ura, 'An On-line adaptation method in a neural network based control system for AUV's,' IEEE Journal of Oceanic Engineering, Vol. 20, No. 3, pp. 211-220, July 1995 https://doi.org/10.1109/48.393077
  9. N. E. Leonard, 'Control synthesis and adaptation for an underactuated autonomous underwater vehicle,' IEEE Journal of Oceanic Engineering, Vol. 20, No. 3, pp. 221-228, July 1995 https://doi.org/10.1109/48.393076
  10. E. B. Kosmatopoulos, M. M. Polycarpou, M. A. Christodoulou, and P. A. Ioannou, 'High-order neural network structures for identification of dynamical systems,' IEEE Transactions on Neural Networks, Vol. 6, No. 2, pp. 422-431, March 1995 https://doi.org/10.1109/72.363477
  11. M. M. Polycarpou and P. A. Ioannou, 'Neural networks as On-line apporximators of nonlinear systems,' Proc. 31st Conference on Decision and Control, pp. 7-12, 1992
  12. R. M. Sanner and J. J. E. Slotine, 'Gaussian networks for direct adaptive control, 'IEEE Transactions on Neural Networks, Vol. 3, No. 6, pp. 837-863, November 1992 https://doi.org/10.1109/72.165588
  13. F. L. Lewis, K. Liu, and A. Yesildirek, 'Neural net robot controller with guaranteed tracking performance,' IEEE Transactions on Neural Networks, Vol. 6, No. 3, pp. 703-715, May 1995 https://doi.org/10.1109/72.377975
  14. M. M. Polycarpou, 'Stable adaptive neural control scheme for nonlinear systems,' IEEE Transactions on Automatic Control, Vol. 41, No. 3, pp. 447-451, March 1996 https://doi.org/10.1109/9.486648
  15. J. Q. Gong, B. Yao, 'Neural network adaptive robust control of nonlinear systems in semi-strict feedback form,' Automatica, Vol. 37, pp. 1149-1160, 2001 https://doi.org/10.1016/S0005-1098(01)00069-3
  16. T. I. Fossen, Guidance and Control of Ocean Vehicles, John Wiley & Sons, 1994
  17. Slotine, J. J. E., and Li, W., Applied Nonlinear Control, New Jersey, Prentice-Hall, 1991
  18. S. W. Hong et al., 'Development of technologies for navigation and manipulator system of a Semi-autonomous underwater vehicle', Tech. Rep. 99-M-DU-21-C-01, KORDI, Daejeon, Korea, 2000(in Korean)