Table of Contents
ISRN Chemical Engineering
Volume 2012, Article ID 693545, 11 pages
http://dx.doi.org/10.5402/2012/693545
Research Article

Tuning and Retuning of PID Controller for Unstable Systems Using Evolutionary Algorithm

1Department of Electronics and Instrumentation Engineering, St. Joseph鈥檚 College of Engineering, Chennai 600 119, India
2Department of Aerospace Engineering, Division of Avionics, MIT Campus, Anna University, Chennai 600 044, India

Received 9 January 2012; Accepted 26 February 2012

Academic Editors: B. Grgur and S. Rodr铆guez-Couto

Copyright © 2012 V. Rajinikanth and K. Latha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • S. Suganya, P. Deepa, and V. Rajinikanth, “Design of PID controller for chemical process-heuristic algorithm approach,” 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), pp. 1001–1004, . View at PublisherView at Google Scholar
  • Xu-Hui Chen, Ejaz Ul Haq, and Jiawei Lin, “Design, modeling and tuning of modified PID controller for autopilot in MAVs,” 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 475–480, . View at PublisherView at Google Scholar
  • R. Kotteeswaran, and L. Sivakumar, “Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization,” Modelling and Simulation in Engineering, vol. 2014, pp. 1–16, 2014. View at PublisherView at Google Scholar
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  • S. Saravanan, and K. Geetha, “Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller,” Circuits and Systems, vol. 07, no. 11, pp. 3670–3684, 2016. View at PublisherView at Google Scholar
  • Santiago Rómoli, Mario Serrano, Francisco Rossomando, Jorge Vega, Oscar Ortiz, and Gustavo Scaglia, “Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor,” Complexity, vol. 2017, pp. 1–16, 2017. View at PublisherView at Google Scholar