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1 Journal of Applied Computer Science

1 1999

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Neural network based adaptive internal model control for nonlinear plants

100%

Ma Z. , Bajić V. B. , Ho D. W. C.

tom Vol. 7, nr 1

63-78

A novel non-parametric adaptive control method for nonlinear plants is proposed. It combines neural network (NN) based identification and internal model control (IMC) strategy. The NN is used to determine on-line an approximation of the unknown nonlinear process model. The NN parameters are updated according to the error between the plant output and the NN output. The NN can track the system output very well, so that an adaptive IMC can be implemented successfully. The design does not require computation of the inverse of the internal model of the process. Instead, it uses only system input-output data and NN output. The effectiveness of the proposed method is illustrated by a simulation experiment.