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Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network)

Received: 30 September 2016     Accepted: 30 November 2016     Published: 21 December 2016
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Abstract

The proportional integral derivative PID controller remodeled using Neural Network and easy hard ware implementation, which will improve the control system in our industries with a high turnover. However, in this work, we propose a non-linear control of stochastic differential equation to Neural Network matching; the model has been validated, evaluated and compared with other existing controllers. The idea is to have control systems that will be able to achieve, improve, reduce waste and that is more flexible in the level of conversion, to be able to track set point change and reject load disturbance in our process industries. This paper represents a preliminary effort to design a simplified neutral network and proportional integral derivative PID control scheme, and modeling, their operational characteristics for a class of non-linear process. At the end we were able to achieve a good result by remodeling the proportional integral derivative PID controller with Neural Network Technique, and connected the plant process control where all the features of the traditional proportional integral derivative PID controller were retained and as well improved using MAT-LAB. The output was fantastic since the waste and loss encored by the process industries was drastically reduced to minimum.

Published in American Journal of Science, Engineering and Technology (Volume 1, Issue 2)
DOI 10.11648/j.ajset.20160102.12
Page(s) 20-26
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

PID, Neural Network, Model, Controller, Simulation, MAT-LAB

References
[1] P. Atherton, Drek "Almost Six Decades in Control Engineering". Control Systems, IEEE. doi: 10.1109/MCS.2014.2359588, December 2014.
[2] K. Narendra and K. Parthasarathy, “Identification and Control of dynamical Systems using Neural Networks”. IEEE Transactions on Neural Networks’. Vol. 1, No. 1, 1990.
[3] M Suzuki, T Yamamoto and T Tsuji. A design of neural-net based PID controller with evolutionary computation. IEICE Trans. Fundamentals. VOL. E87-A, No. 10, October 2004.
[4] S Mall and S Chakraverty, “Regression Based Neural networktraining for the solution of ordinary differential equations,”International Journal ofMathematicalModelling and NumericalOptimisation, vol. 4, pp. 136–149, 2013.
[5] NSmaouiand Al-EneziS., “Modelling the dynamics of nonlinearpartial differential equations using neural networks,” Journalof Computational and Applied Mathematics, vol. 170, no. 1, pp. 27–58, 2004.
[6] M Kumarand NYadav “Multilayer perceptrons and radialbasis function neural network methods for the solution ofdifferential equations: a survey,” Computers and Mathematicswith Applications, vol. 62, no. 10, pp. 3796–3811, 2011.
[7] J Mahan andK. S McFall “Artificial neural networkmethodfor solution of boundary value problems with exact satisfactionof arbitrary boundary conditions,” IEEE Transactions on Neural Networks, vol. 20, no. 8, pp. 1221–1233, 2009.
[8] I. G Tsoulos, D. Gavrilis, and E. Glavas, “Solving differentialequations with constructed neural networks,” Neurocomputing, vol. 72, no. 10–12, pp. 2385–2391, 2009.
[9] S. A. Hodaand H. A Nagla “Neural network methods formixed boundary value problems,” International Journal ofNonlinear Science, vol. 11, pp. 312–316, 2011.
[10] A. J. Meade Jr. and A. A. Fernandez, “Solution of nonlinear ordinary Differential equations by feed forward neural networks,” Mathematical and Computer Modelling, vol. 20, no. 9, pp. 19–44, 1994.
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  • APA Style

    Uchegbu C. E., Eneh I. I., Ekwuribe M. J., Ugwu C. O. (2016). Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network). American Journal of Science, Engineering and Technology, 1(2), 20-26. https://doi.org/10.11648/j.ajset.20160102.12

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    ACS Style

    Uchegbu C. E.; Eneh I. I.; Ekwuribe M. J.; Ugwu C. O. Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network). Am. J. Sci. Eng. Technol. 2016, 1(2), 20-26. doi: 10.11648/j.ajset.20160102.12

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    AMA Style

    Uchegbu C. E., Eneh I. I., Ekwuribe M. J., Ugwu C. O. Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network). Am J Sci Eng Technol. 2016;1(2):20-26. doi: 10.11648/j.ajset.20160102.12

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  • @article{10.11648/j.ajset.20160102.12,
      author = {Uchegbu C. E. and Eneh I. I. and Ekwuribe M. J. and Ugwu C. O.},
      title = {Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network)},
      journal = {American Journal of Science, Engineering and Technology},
      volume = {1},
      number = {2},
      pages = {20-26},
      doi = {10.11648/j.ajset.20160102.12},
      url = {https://doi.org/10.11648/j.ajset.20160102.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20160102.12},
      abstract = {The proportional integral derivative PID controller remodeled using Neural Network and easy hard ware implementation, which will improve the control system in our industries with a high turnover. However, in this work, we propose a non-linear control of stochastic differential equation to Neural Network matching; the model has been validated, evaluated and compared with other existing controllers. The idea is to have control systems that will be able to achieve, improve, reduce waste and that is more flexible in the level of conversion, to be able to track set point change and reject load disturbance in our process industries. This paper represents a preliminary effort to design a simplified neutral network and proportional integral derivative PID control scheme, and modeling, their operational characteristics for a class of non-linear process. At the end we were able to achieve a good result by remodeling the proportional integral derivative PID controller with Neural Network Technique, and connected the plant process control where all the features of the traditional proportional integral derivative PID controller were retained and as well improved using MAT-LAB. The output was fantastic since the waste and loss encored by the process industries was drastically reduced to minimum.},
     year = {2016}
    }
    

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    AU  - Eneh I. I.
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    AB  - The proportional integral derivative PID controller remodeled using Neural Network and easy hard ware implementation, which will improve the control system in our industries with a high turnover. However, in this work, we propose a non-linear control of stochastic differential equation to Neural Network matching; the model has been validated, evaluated and compared with other existing controllers. The idea is to have control systems that will be able to achieve, improve, reduce waste and that is more flexible in the level of conversion, to be able to track set point change and reject load disturbance in our process industries. This paper represents a preliminary effort to design a simplified neutral network and proportional integral derivative PID control scheme, and modeling, their operational characteristics for a class of non-linear process. At the end we were able to achieve a good result by remodeling the proportional integral derivative PID controller with Neural Network Technique, and connected the plant process control where all the features of the traditional proportional integral derivative PID controller were retained and as well improved using MAT-LAB. The output was fantastic since the waste and loss encored by the process industries was drastically reduced to minimum.
    VL  - 1
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Author Information
  • Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria

  • Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria

  • Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria

  • Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria

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