Model Reference Control Using Cmac Neural Networks
ICANN'07: Proceedings of the 17th international conference on Artificial neural networks(2007)
Abstract
This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.
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Key words
CMAC neural network,CMAC model,dynamic nonlinear model,model reference control system,experimental input-output data,nonlinear system,successful experimental result,system identification,fluid height,fundamental physical law
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