Volume: 5, Issue: 1(1994)
pp. 77-82 DOI: 10.1142/S0129065794000098
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Full Text (PDF, 377KB)
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| Title: |
MODEL REFERENCE DIRECT ADAPTIVE CONTROL OF NONLINEAR PLANTS USING NEURAL NETWORKS |
| Author(s): |
MOHAMMAD BAHRAMI
School of Electrical Engineering, University of New South Wales, P.O. Box 1, Kensington, 2033 NSW, AustraliaKEITH E. TAIT
School of Electrical Engineering, University of New South Wales, P.O. Box 1, Kensington, 2033 NSW, Australia
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| History: |
Received 14 August 1992
Revised 18 October 1993
Accepted 8 December 1993
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| Abstract: |
A learning scheme for multilayer feedforward neural networks used as direct adaptive controllers of nonlinear plants is suggested. This scheme is a supervised steepest descent one that does not require backpropagation of the error. Using a neural network controller trained with this method does not require the identification stage and this makes it superior to the other methodologies. Methods for using neural networks in plant control suggested in the literature are discussed and compared with the proposed system. The structure of the network and the training method used are explained. Simulations based on model reference control of some nonlinear plants show satisfactory performance. |
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