Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31231
CSTR Control by Using Model Reference Adaptive Control and PSO

Authors: Neha Khanduja

Abstract:

This paper presents a comparative analysis of continuously stirred tank reactor (CSTR) control based on adaptive control and optimal tuning of PID control based on particle swarm optimization. In the design of adaptive control, Model reference adaptive control (MRAC) scheme is used, in which the adaptation law have been developed by MIT rule & Lyapunov’s rule. In PSO control parameters of PID controller is tuned by using the concept of particle swarm optimization to get optimized operating point for minimum integral square error (ISE) condition. The results show the adjustment of PID parameters converting into the optimal operating point and the good control response can be obtained by the PSO technique.

Keywords: Particle Swarm Optimization (PSO), Optimal control, Model reference adaptive control (MRAC)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109852

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1745

References:


[1] Rahul Upadhyay, Rajesh Singla, “application of adaptive control in a process control”, 2nd international conference on education technology and computer (ICETE), 2010. (IEEE).
[2] R. Aruna, M. Senthil Kumar, “Adaptive Control for interactive thermal process “proceedings of ICTECT, 2011. (IEEE)
[3] Karl J. Astrom and Bjorn Witten mark, Adaptive control, second edition, Pearson Education, 2001.
[4] K. A. and J. C. Kantor, “An exothermic continuous stirred tank reactor is feedback equivalent to a linear system,” Chem. Eng. Commum., vol. 37, no. 1, 1985.
[5] Dr. M. J. Willis, “continuous stirred tank reactor models”, Deptt. of Chemical and Process Engineering, University of Newcastle, March 2010.
[6] K. Prabhu, Dr. V. Murali Bhaskaran, “optimization of control loop using adaptive method”, International Journal Of Engineering and Innovative Technology, Volume 1, Issue 3, March 2012.
[7] S. Jegan, K. Prabhu, “Temperature control of CSTR process using adaptive control”, International Conference on Computing and Control Engineering (ICCCE), 2012.
[8] Mohammad Ali Nekoui, Mohammad Ali Khamene and Mohammad Hosein Kazemi, “Optimal Design of PID controller for a CSTR System Using Particle Swarm Optimization”, International Power Electronics and Motion Control Conference (EPE-PEMC), 2010.
[9] G. Glan Devadhas, S. Pushpakumar, S. V. Muruga Prasad, “Intelligent Computation of Controller Using Optimization Technique for a Nonlinear Chemical Process”, International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC) Vol. 1, No.3, September 2011.
[10] J. Kennedy, & R.C. Eberhart, Particle swarm optimization, IEEE Proceedings International Conference on Neural Networks (ICNN’95), No.4, Perth, Australia, pp.1942–1948, 1995.
[11] Liu, Y., Zhang, J. and Wang, S. (2004). Optimization design based on PSO algorithm for PID controller. Proceedings of5th World Congress on Intelligent Control and Automation, Vol. 3, 2419-2422.
[12] Geetha. M, Balajee. K. and Jovitha Jerome, “Optimal Tuning of Virtual Feedback PID Controller for a Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization(PSO) Algorithm, IEEE-International Conference On Advances In Engineering, Science And Management (lCAESM -2012) March, 2012
[13] Thomas Beielstein, K. E. Parsopoulos and Michael N. Vrahatis, “Tuning PSO Parameters through Sensitivity Analysis, Technical Report of the Collaborative Research Center 531 Computational Intelligence CI-- 124/02, University of Dortmund, January (2002).
[14] Y Zheng, Liyan Zhang, Jixin Qian Longhua Ma “Robust PID Controller Design using PSO” International Symposium on Intelligent Control IEEE Oct (2003)
[15] T. Bartz–Beielstein K.E. Parsopoulos and M.N. Vrahatis, “Analysis of Particle Swarm Optimization Using Computational Statistics”, International conference on numerical analysis and applied mathematics ICNAAM-(2004).
[16] Neha Khanduja,”CSTR control using model reference adaptive control & bio inspired optimization Technique”, M. techthesis, D. T. U., July, 2013.