Improving PID Controller Using Neural Network Technique

Authors(3) :-Uchegbu C. E, Ekwuribe J. M, Ogbonnaya I. J

This research focuses on improving the quality of traditional Proportional integral derivative (PID) using neural network. A Reverence model approach was used to design a Neural Network NN controller and a proportional integral derivative PID controller. The purpose is to have a stable control systems in our industries that will help to improve and reduce waste during production in our industries that will be more flexible in the level of conversion, to be able to track set point change and reject load disturbance in our process industries. PID control scheme was used as a benchmark to study the performance of the PID controller at the same time with equivalent neural network. The proportional Integral derivative controller PID was modeled using Neural Network Technique NN and a MAT-LAB simulation was carried out and observation showed that there was a great improvement on the traditional PID controller as it started functioning like a digital controller. When connected to the Plant process control were all features of the traditional proportional integral derivative PID controller were retained and as well improved using Neural Network . The output was fantastic since the waste and loss encored by the process industries was drastically reduced to minimal.

Authors and Affiliations

Uchegbu C. E
Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria
Ekwuribe J. M
Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria
Ogbonnaya I. J
Department of Electrical and Electronic Engineering, Abia State Polytechnic, Aba, Nigeria

PID, NN, Simulation, MAT-LAB

  1. J. Meade Jr. and A. A. Fernandez,( 1994). "The numerical solution of linear ordinary differential equations by feed forward neural networks," Mathematical and Computer Modeling, vol. 19, no. 12, pp. 1–25,
  2. J.Meade Jr. and A. A. Fernandez, (1994) "Solution of nonlinear ordinary Differential equations by feed forward neural networks," Mathematical and Computer Modelling, vol. 20, no. 9, pp. 19–44,.
  3. R. Parisi, M. C.Mariani, andM. A. Laborde, (2003) "Solving differential equations with unsupervised neural networks," Chemical Engineering and Processing, vol. 42, no. 8-9, pp. 715–721.
  4. Ebrahimi M, (2000) Analysis, modeling and simulation of stiffness in machine tool drive [J]. Computers?Industrial Engineering,
  5. C. Chen,, Zhang, L., & Hao, N.M. et al. (2003) Application of Neural network PID Controller in Constant Temperature and Constant Liquid-level System [J]. Micro-computer information, , 19(1): 23-24, 42.
  6. Lee and I. S. Kang, (1990). "Neural algorithm for solving differential equations," Journal of Computational Physics, vol. 91, no. 1, pp. 110–131,
  7. E. Lagaris, A. C. Likas, and D. G. Papageorgiou, (2000) "Neural network method for boundary value problems with irregular Boundaries," IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1041–1049,.
  8. M. Zurada, (1994). Introduction to Artificial Neural Network, West Publishing,
  9. S.McFall and J. R. Mahan, (2009). "Artificial neural network method for solution of boundary value problems with exact satisfaction of arbitrary boundary conditions," IEEE Transactions on NeuralNetworks, vol. 20, no. 8, pp. 1221–1233,
  10. Narendra, Parthasarathy, K. (1990). "Identification and Control of ynamical Systems using Neural Networks". IEEE Transactions on Neural Nerworks. Vol. 1, No. 1.
  11. Jianyu, L. Siwei, Q. Yingjian, and H. Yaping, (2003) "Numerical solution of elliptic partial differential equation using radial basis function neural networks," Neural Networks, vol. 16, no. 5-6, pp. 729–734,
  12. Suzuki,, Yamamoto, T., & Tsuji, T. (2004). A design of neural-net based PID controller with evolutionary computation. IEICE Trans. Fundamentals. VOL. E87-A, No. 10 October.
  13. Smaoui and S. Al-Enezi, (2004) "Modelling the dynamics of nonlinear partial differential equations using neural networks," Journal of Computational and Applied Mathematics, vol. 170, no. 1, pp. 27–58,
  14. Selvaraju and J. Abdul Samant, (2010). "Solution of matrix Riccati differential equation for nonlinear singular system using neural networks," International Journal of Computer Applications, vol. 29, pp. 48–54,
  15. Dwyer, Aidan (2006). PI and PID controller tuning rules: an overview and personal perspective. Proceedings of the IET Irish Signals and Systems Conference, pp. 161-166, Dublin Institute of Technology.
  16. Dwyer, Aidan and Ringwood, John: July, (1999) A classification of techniques for the compensation of time delayed processes. Part 1: Parameter optimised controllers. Proceedings of the 3rd IMACS/IEEE International Multiconference on Circuits, Systems, Communications and Computers, Athens, Greece, [in Modern Applied `Mathematical
  17. Uchegbu C. E et al.: 2016 Remoldelling of PID Controller Based on an Artificial Intelligency (Neural Network) American Journal of Science, Engineering and Technology.
  18. P. Singh, S. Chakraverty, R. K. Sharma, and G. K. Sharma, (2009) "Modeling vibration frequencies of annular plates by regression based neural network," Applied Soft Computing Journal, vol. 9, no. 1, pp. 439–447

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2016
Date of Publication : 2016-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 576-580
Manuscript Number : IJSRSET1626152
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Uchegbu C. E, Ekwuribe J. M, Ogbonnaya I. J, " Improving PID Controller Using Neural Network Technique, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 6, pp.576-580, November-December-2016.
Journal URL :

Article Preview

Follow Us

Contact Us