Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Neural Network Control for PPDCV Clinker Cooler System

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 2
Year of Publication: 2014
Majid Ahmed Oleiwi
Zaid Abed Aljasem

Majid Ahmed Oleiwi and Zaid Abed Aljasem. Article: Neural Network Control for PPDCV Clinker Cooler System. International Journal of Computer Applications 85(2):29-34, January 2014. Full text available. BibTeX

	author = {Majid Ahmed Oleiwi and Zaid Abed Aljasem},
	title = {Article: Neural Network Control for PPDCV Clinker Cooler System},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {2},
	pages = {29-34},
	month = {January},
	note = {Full text available}


Pilot Proportional Directional Control Valve (PPDCV) is used to control the clinker cooler actuator position for Cement Industries. PID controllers are satisfying control for the actuator position control with transient response and reduce steady state error. Uncertainty parameter cause unsmooth response small oscillation in actuator position. This work explains a neural network control for (PPDCV) system with uncertainty parameter, reduces the disturbance, and removes the oscillation in the response. The paper is focused on the possibilities of applying trained artificial neural networks for creating the system inverse models that are used to design inverse control algorithm for non-linear dynamic system. The simulation experiment is performed using Matlab and Simulink to show the response of these controllers.


  • Yung C. Shin and Chengying Xu. 2008. Intelligent Systems Modeling, Optimization, and Control. Taylor & Francis Group, LLC.
  • M. Norgaard, et al. 2000. Neural Networks for Modeling and Control of Dynamic Systems. A Practitioner's Handbook. Springer, Boston, MA.
  • O. A. Dahunsi, J. et al. September 2010. System Identification and Neural Network Based PID Control of Servo - Hydraulic Vehicle Suspension System. South African Institute of Electrical Engineers; 101(3). 93-105.
  • A. A. Mozafari and M. Lahroodi. April 2008. Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks. IJE Transactions B. Applications; 21(1). 71-84.
  • Ammar A. Aldair and Weiji J. Wang. June 2011. Neural Controller Based Full Vehicle Nonlinear Active Suspension Systems with Hydraulic Actuators. International Journal of Control and Automation; 4(2). 79-94.
  • A. Khajekaramodin et al. Aug 2007. Semi-active Control of Structures Using Neuro-Inverse Model of MR Dampers. First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi in University of Mashhad.
  • Wenjun Meng, Zhanlin and Wang Lihua Qiu. June 18-21, 2007. Analysis for Neural Network Controllers and Passivity-Based Controller on Test System for Aero Hydraulic Pump. 12thIFToMM World Congress, Besançon (France).
  • Zhen-Yuan, et al. 2010. Characteristics Forecasting of Hydraulic Valve Based on Grey Correlation and ANFIS. Expert Systems with Applications 37. 1250–1255.
  • Jyh-Chyang,et al. 2008. Modeling and Control of a New 1/4T Servo-Hydraulic Vehicle Active Suspension System. Journal of Marine Science and Technology; 15(3). 265-272.
  • Parker Hannifin GmbH & Co. KG. 2009. Manual Series DF plus XG099.
  • Majid Ahmed Oleiwi and Zaid Abed Aljasem. September 2013. Position Control for (PPDCV) Using PID & Fuzzy Supervisory Controller. Academic Research International; 4(5). 110-128.
  • Bohdan T. et al. 2007. Dynamic Modeling and Control of Engineering Systems. Third Edition, John F. Gardner,.
  • Mark Hudson Beale, et al. 2013. Neural Networks Toolbox User's Guide. The Math Works, Inc.
  • Lera G. and M. Pinzolas. 2002. Neighborhood Based Levenberg-Marquardt Algorithm for Neural Network Training. IEEE Transactions on Neural Networks; 13(5). 1200-1203.
  • D. Scholz. 1996. Proportional hydraulics. Festo Didactic KG, D73734 Esslingen, Textbook.