Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network)
American Journal of Science, Engineering and Technology
Volume 1, Issue 2, December 2016, Pages: 20-26
Received: Sep. 30, 2016;
Accepted: Nov. 30, 2016;
Published: Dec. 21, 2016
Views 4049 Downloads 144
Uchegbu C. E., Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria
Eneh I. I., Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria
Ekwuribe M. J., Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria
Ugwu C. O., Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria
Follow on us
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.
PID, Neural Network, Model, Controller, Simulation, MAT-LAB
To cite this article
Uchegbu C. E.,
Eneh I. I.,
Ekwuribe M. J.,
Ugwu C. O.,
Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network), American Journal of Science, Engineering and Technology.
Vol. 1, No. 2,
2016, pp. 20-26.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
P. Atherton, Drek "Almost Six Decades in Control Engineering". Control Systems, IEEE. doi: 10.1109/MCS.2014.2359588, December 2014.
K. Narendra and K. Parthasarathy, “Identification and Control of dynamical Systems using Neural Networks”. IEEE Transactions on Neural Networks’. Vol. 1, No. 1, 1990.
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.
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.
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.
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.
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.
I. G Tsoulos, D. Gavrilis, and E. Glavas, “Solving differentialequations with constructed neural networks,” Neurocomputing, vol. 72, no. 10–12, pp. 2385–2391, 2009.
S. A. Hodaand H. A Nagla “Neural network methods formixed boundary value problems,” International Journal ofNonlinear Science, vol. 11, pp. 312–316, 2011.
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.