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Optimized deep learning neural network predictive controller for continuous stirred tank reactor
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DOI：10.1016/j.compeleceng.2017.07.004.png)
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Abstract
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In this paper, a deep learning neural network model predictive controller (DLNNMPC) is designed to analyse the performance of a non-linear continuous stirred tank reactor (CSTR) that performs parallel and series reactions. The data generated employing the state space model of CSTR is used to train the designed deep learning neural network controller. Deep Learning Neural Network (DLNN) progresses the training with its weights tuned by the proposed hybrid version of evolutionary algorithms - Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The developed hybrid PSO - GSA based DLNN model of continuous stirred tank reactor is employed in this paper for model predictive controller design. The effectiveness of the proposed DLNNMPC tuned by hybrid PSO - GSA for CSTR is validated for its performance on comparison with that of other designed Proportional - Integral (PI) and Proportional - Integral - Derivative (PID) controllers as available in early literatures for the same problem under consideration. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Continuous stirred tank reactor
Deep learning neural network controller
Model predictive control
Particle swarm optimization
Gravitational search algorithm
Controller model
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