Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization

  • Khulood A. Dagher Department of Computer Science / College of Science / University of Baghdad
  • Ahmed S. Al-Araji Department of Control and Systems Engineering / University of Technology
Keywords: Particle Swarm Optimization, PID Controller, Neural Network, CSTR

Abstract

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

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Published
2013-12-31
How to Cite
Dagher, K., & Al-Araji, A. (2013). Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization. Al-Khwarizmi Engineering Journal, 9(4), 46- 53. Retrieved from https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/184
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Articles