ANN based IMC scheme for CSTR

Authors:
Tarun Varshney;Ruchi Varshney;Satya Sheel
Affiliations:
Moradabad Institute of Technology, Moradabad, India;Moradabad Institute of Technology, Moradabad, India;Motilal Nehru National Institute of Technology, Allahabad, India
Venue:
Proceedings of the International Conference on Advances in Computing, Communication and Control
Year:
2009

Citing 3
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Neural networks for control systems: a survey

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An approximate internal model-based neural control for unknown nonlinear discrete processes

IEEE Transactions on Neural Networks

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Abstract

This paper demonstrates that neural networks can be used effectively for control of nonlinear dynamical systems. The proposed control scheme is based on the artificial neural network and is applied to an isothermic continuous stirred tank reactor (CSTR). In this paper we have tested the internal model control (IMC) strategy based on neural networks for process systems. This approach of control uses two Feed Forward Neural networks (FFNN), one as an identifier and the other as a controller. Multilayer neural network has been used for forward modeling and the inverse model of the process which has been determined off line using input output data of process, as controller. The modified back propagation algorithm has been used to train the neural networks. Neural network based IMC scheme has been implemented for both set point and regulatory control action and the comparison have been made for a set of constant momentum term.