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• [P1057] RBF Neural Network Adaptive Backstepping Controllers For MIMO Nonlinear Systems
  Author(s): Wei-Yen Wang, Ming Feng Kuo, Hong Chin-Ming, Yih-Guang Leu
  
  

  Abstract
  This paper proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. The problem may result in producing the unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference output can be made arbitrarily small.

  
   

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