Autosoft Journal

Online Manuscript Access

Tuning of a PID Controller using Soft Computing Methodologies Applied to Moisture Control in Paper Machine



Proportional -Integral -Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Bacterial Foraging Optimization and heuristic algorithm bacterial foraging combine with particle swarm optimization. The proposed soft computing is used to tune the PID parameters and its performance has been compared with the conventional method Ziegler Nichols. The results obtained reflect that use of soft computing based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on moisture control system in paper machine (Machine Direction).



Total Pages: 13
Pages: 399-411


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 18
Issue: 4
Year: 2012

Cite this document


Lelic M. A. Reference Guide to PID Controllers in the Nineties, In Proceedings of IFAC Workshop: Past, Present and Future of PID Contro1.75–78, 2000.

Ziegler, J.G. and Nichols. N.B. Optimum settings for Automatic Controllers. Trans. ASME.64, 1942.

Astrom K.J. and T. Hagglund. PID Controllers: Theory, Design, and Tuning. Triangle Park, 140–148, 1995.

Ellis G. Control systems design guide. Academic Press, London.1991.

Jukka Lieslehto. PID Controller Tuning Using Evolutionary Programming, Proceedings of the American Control Conference Arlington. 2828–2833, 2001.

Seema Nara, Pooja Khatri, Jatin Garg. Proportional -Integral - Deriative Controller Tuning of Temperature Control System Using Genetic Algorithm, International Journal of Electronics, Information and System,l2, 37–42, 2010.

Popov, A., A. Farag, and H. Werner. "Tuning of a PID Controller Using a Multi-Objective Optimization Technique Applied to A Neutralization Plant." Proceedings of the 44th IEEE Conference on Decision and Control n. pag. Crossref. Web.

Jin-Sung Kim, Jin-Hwan Kim, Ji-Mo Park, Sung-Man Park, Won-Yong Choe. Auto Tuning PID controller based on Improved Genetic Algorithm for Reverse Osmosis Plant, World Academy of Science, Engineering and Technology .47, 384–389, 2008.

Neenu Thomas, Dr. P. Poongodi. Position Control of Motor Using Genetic Algorithm Based PID Controller, Proceeding of the World Congress on Engineering. 2, 2009.

Mehdi Nasri, Hossein Nezamabad-pour, and Malihe Maghfoori. A PSO-Based Optimum Design of PID Controller for a Linear Brushless DC Motor, World Academy of Science, Engineering and Technology 26, 210–218, 2007.

Pi11ay.N .A. Particle Swarm Optimization approach Tuning of SISO PID control loops, Master thesis Light Current Electronics, Durban University of Technology, 2009.

Giriraj Kumar, S. M. et al. "Particle Swarm Optimization Technique Based Design of Pi Controller for a Real-Time Non-Linear Process." Instrumentation Science & Technology 36.5 (2008): 525-542. Crossref. Web.

Salim Ali E. and S.M. Abad-Elazin. Optimal PID Tuning for Load Frequency Control Using Bacteria Foraging Optimization Algorithm, Conference Proceedings (MEPCON’ 10), Cairo University, Egypt, 19–21, 2010.

Kim, Dong Hwa, Ajith Abraham, and Jae Hoon Cho. "A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization." Information Sciences 177.18 (2007): 3918-3937. Crossref. Web.

Tushar Jain and M.J. Nigam. Optimization of PD-PI Controller Using Swarm Intelligence, Journal of Theoretical and Applied Information Technology, 1013–1018, 2007.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048