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Uncertainty-Compensating Neural Network Control for Nonlinear Systems

비선형 시스템의 불확실성을 보상하는 신경회로망 제어

  • Cho, Hyun-Seob (Dept. of Digital Broadcasting & Electronic Engineering, Chungwoon Univ.)
  • 조현섭 (청운대학교 디지털방송공학과)
  • Received : 2009.12.31
  • Accepted : 2010.05.13
  • Published : 2010.05.31

Abstract

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

References

  1. Howard Demuth,Mark Beale,"Neural Network ToolBox for Use with MATLAB"The MathWorks Inc.,2008
  2. Chen S,Billing S A,"Grant P M.Recursive Hybrid Algorithm for Nonlinear System Identification using Radial Function Network", Int.JControl,1992,55(5): 105-1070
  3. G.A.Montague,M.J.Willis and A.J.Morris, "Artificial Neural Network Model Based Control"Automatic Control Conference,1998
  4. M.M.Gupta and D.H.Rao,"Dynamic Neural Units in the Control of Linear and Nonlinear Systems."In Proceedings of the International Joint Conf. On Neural Networks,June 2005,pp.100-105
  5. D.Andes,B.Widrow,M.Lehr and E.Wan,"MR III:A Robust Algorithm for Training Analog Neural Networks" International Joint Conference on Neural Networks,2000
  6. K.S.Narendra and K.Parthasarathy, "Identification and Control of Dynamical Systems Using Neural Networks" IEEE Trans on Neural Network Vol.1 No.1,pp.4-27,2002
  7. J. Moody and C.J.Darken, "Fast learning in Networks of Locally-Tuned Processing Units" Neural Computation, Vol.1, pp.281-194, 2005.