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Optimization Algorithm-Based Elman Neural Network Controller for Continuous Stirred Tank Reactor Process Model


Affiliations
1 Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641 407, India
 

A continuous stirred tank reactor (CSTR) is a standout nonlinear system among the most essential units of chemical industries. In this article, an Elman neural network is designed to analyse the characteristics of nonlinear behaviour of the CSTR system. The data generated employing the state-space model of CSTR are used to train the designed Elman neural network controller and the controller parameters are optimally tuned by the proposed hybrid swarm intelligencebased optimization algorithm. Two different hybridizations have been developed, including DPSO, DGSA and hybrid DPSO–DGSA and successfully employed in controller tuning. The significance of the proposed controller is validated by a comparative analysis made with conventional methods and the performance is experimentally demonstrated using MATLAB software.

Keywords

Continuous Stirred Tank Reactor, Neural Network Controller, Nonlinear Behaviour, Optimization Algorithm, Process Control.
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  • Optimization Algorithm-Based Elman Neural Network Controller for Continuous Stirred Tank Reactor Process Model

Abstract Views: 305  |  PDF Views: 122

Authors

I. Baranilingesan
Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641 407, India

Abstract


A continuous stirred tank reactor (CSTR) is a standout nonlinear system among the most essential units of chemical industries. In this article, an Elman neural network is designed to analyse the characteristics of nonlinear behaviour of the CSTR system. The data generated employing the state-space model of CSTR are used to train the designed Elman neural network controller and the controller parameters are optimally tuned by the proposed hybrid swarm intelligencebased optimization algorithm. Two different hybridizations have been developed, including DPSO, DGSA and hybrid DPSO–DGSA and successfully employed in controller tuning. The significance of the proposed controller is validated by a comparative analysis made with conventional methods and the performance is experimentally demonstrated using MATLAB software.

Keywords


Continuous Stirred Tank Reactor, Neural Network Controller, Nonlinear Behaviour, Optimization Algorithm, Process Control.

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DOI: https://doi.org/10.18520/cs%2Fv120%2Fi8%2F1324-1333