Journal of Advances in Information Technology, Vol 3, No 1 (2012), 48-56, Feb 2012
doi:10.4304/jait.3.1.48-56

Multivariable control of nonlinear process using soft computing techniques

N. Kamala, T. Thyagarajan, S. Renganathan

Abstract


In this paper, Particle Swarm Optimization (PSO) is utilized to optimize the coefficients of a decentralized PID controller for a nonlinear process by minimizing the Integral Square Error (ISE).The controller is tuned at chosen operating points, which are selected to cover the nonlinear range of the process. The optimal PID controller parameters are gain scheduled using a Fuzzy Gain scheduler. The effectiveness of the proposed control scheme has been demonstrated by conducting simulation studies on a Continuous Stirred Tank Reactor (CSTR) process which exhibits dynamic nonlinearity. It is shown that the proposed controller provides better set point tracking and load disturbance rejection than the Internal Model Control (IMC) based conventional control scheme.



Keywords


CSTR, Decentralized control, PSO, IMC, PID, Integral Square Error, Fuzzy Gain scheduling

References


Xiong Q., W.J.Cai, M.J.He.,”Equivalent transfer function method for PI/PID controller design of MIMO processes”, Int.journal of Process control, 17, pp665-673. (2007)

[2] Halevi.Y. Z.JPalmor and T.Efrati, “Automatic tuning of Decentralized PID controllers for MIMO processes”.Int.journal of Processing control, 7, No2, pp119-128, (1997)

[3] Kamala,N, Thyagarajan,T and Renganathan,S, “Multi-variable control of Nonlinear Process”, Int.J.of Recent Trends in Engineering and Technology,vol.4,No.4,pp 106-110,(2010)

[4] Qiang Xiong, Wen-Jian Cai, Ming He, A practical Decen-tralized PID Auto tuning Method for TITO systems under closed loop control, International Journal of Innovative computing, Information and Control.vol 2, number 2, April 2006.

[5] Zhao Zy M., Tomizuka and Isaka S., ‘Fuzzy Gain Schedul-ing of PID Controllers’, Proceedings of the first IEEE con-ference on Control Applications, Dayton, Ohio, pp. 693-703, (1992)

[6] Visioli.A., “Tuning of PID controllers with Fuzzy Logic”, IEE-Proc.Control Theory Appl.Vol.148, No1, pp.1-8, (2001)
http://dx.doi.org/10.1049/ip-cta:20010232

[7] Blanchett, T.P., Kember.G.C, Dubey.R, “PID Gain Sche-duling using Fuzzy Logic”, ISA Transactions, Vol.39, pp.317-325, (2000)
http://dx.doi.org/10.1016/S0019-0578(00)00024-0

[8] Su C.T.,J.T.Wong,”Designing MIMO controller by neuro-travelling particle swarm optimizer approach”, Int.J.of Ex-pert systems with applications,32,pp848-855, (2007).

[9] Chao ou, Weixing Lin, “Comparison between PSO and GA for parameters optimization of PID controller”, Proc.IEEE Int. Conf. on Mechatronics and Automation, pp2471-2475(2006).

[10] M.W.Iruthayarajan,S.Bhaskar, “Evolutionary algorithms based design of multivariable PID controller”, Expert sys-tems with Applications,36(2009)9159-9167
http://dx.doi.org/10.1016/j.eswa.2008.12.033

[11] Ogunnaike, B.A, and Ray, W.H.: Process Dynamics, Mod-eling and Control, Oxford University Press, New York, (1994).

[12] M.Pottman and D.E.Seborg, “Identification of nonlinear process using Reciprocal Multi Quadratic Functions”, Journal of Process Control, 2, pp.189-203, (1992)
http://dx.doi.org/10.1016/0959-1524(92)80008-L


Full Text: PDF


Journal of Advances in Information Technology (JAIT, ISSN 1798-2340)

Copyright @ 2006-2014 by ACADEMY PUBLISHER – All rights reserved.