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Neural Networks Based Control of Nonlinear Process

Andy Srinivasan*, Sowrirajan N.**

*-** Department of Electronics and Instrumentation Engineering, SRM Valliammai Engineering College, Chennai, India.

Periodicity:August - October'2019
DOI : https://doi.org/10.26634/jic.7.4.16816

Abstract

The artificial neural network based Predictive and Inverse model controller schemes are applied to the simulation of the time-dependent behavior of an isothermal Continuous Stirred Tank Reactor (CSTR). In the first scheme, a methodology is proposed for training and prediction of dynamic behavior of isothermal CSTR using feedforward neural network. Then a nonlinear one step predictive control strategy based on identified model is proposed for CSTR control. In the second scheme, neural network based inverse model controller is presented. Here, the controller is a rearrangement of the plant model. The performances of both neural controllers are evaluated in simulation for both servo and regulatory problems and the results are compared with PID controller.

Keywords

Neural Networks, CSTR, Predictive Control, NARMA and PID Control.

How to Cite this Article?

Srinivasan, A., and Sowrirajan, N. (2019). Neural Networks Based Control of Nonlinear Process. i-manager's Journal on Instrumentation and Control Engineering, 7(4), 18-25. https://doi.org/10.26634/jic.7.4.16816

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