Multiobjective Optimization of Multiloop Fractional Order PID Controller Tuned Using Bat Algorithm for Two Interacting Conical Tank Process

S.K. Lakshmanaprabu; U. Sabura Banu

doi:10.4028/www.scientific.net/AMM.704.373

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 704Multiobjective Optimization of Multiloop...

Multiobjective Optimization of Multiloop Fractional Order PID Controller Tuned Using Bat Algorithm for Two Interacting Conical Tank Process

Abstract:

Multiloop fractional order PID controller is tuned using Bat algorithm for two interacting conical tank process. Two interacting conical tank process is modelled using mass balance equations. Two Interacting Conical Tank process is a complex system involving tedious interaction. Straight forward multiloop PID controller design involves various steps to design the controller. Due to easy implementation and quick convergence, Bat algorithm is used in recent past for solving continuous non-linear optimization problems to achieve global optimal solution. Bat algorithm, a swarm intelligence technique will be attempted to tune the multiloop fractional order PID controller for two interacting conical tank process. The multi objective optimized multiloop fractional PID controller is tested for tracking, disturbance rejection for minimum Integral time absolute error.

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Periodical:

Applied Mechanics and Materials (Volume 704)

Pages:

373-379

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.704.373

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Online since:

December 2014

Authors:

S.K. Lakshmanaprabu*, U. Sabura Banu

Keywords:

Bat Algorithm, Multi-Loop Fractional Order PID Controller, Two Interacting Conical Tank Process

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References

[1] Ziegler J. G, Nichols, N.B.: Optimum setting for automatic controllers, Transaction of ASME, Vol. 62, pp.759-768. (1942).

Google Scholar

[2] K.J. Åström, T. Hägglund, Revisiting the Ziegler–Nichols step response method for PID control, Journal of Process Control Vol. 14 (6), p.635–650(2004).

DOI: 10.1016/j.jprocont.2004.01.002

Google Scholar

[3] T.B. Sekara, M.R. Matausek : Revisiting the Ziegler-Nichols process dynamics characterization, Journal of process control, Vol. 20, pp.360-363(2010).

DOI: 10.1016/j.jprocont.2009.08.004

Google Scholar

[4] P. Nordfeldt, T. Hagglund : Decoupler and PID controller design of TITO systems, Journal of Process Control, Vol. 16, pp.923-936 (2006).

DOI: 10.1016/j.jprocont.2006.06.002

Google Scholar

[5] Branislav T. Jevtovic, Miroslav R. Matausek : PID controller design of TITO system based on ideal decoupler, Journal of process control, Vol. 20, pp.869-876 (2010).

DOI: 10.1016/j.jprocont.2010.05.006

Google Scholar

[6] E.P. Nahas, M.A. Henson , D.E. Seborg : Nonlinear internal model control strategy for neural network models, Computer & Chemical Engineering , Vol. 16, pp.1039-1057(1992).

DOI: 10.1016/0098-1354(92)80022-2

Google Scholar

[7] Bin Hu, Zhao Zhao, Jun Liang : Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model, Journal of Process Control , Vol. 22 , p.207– 217 (2012).

DOI: 10.1016/j.jprocont.2011.09.002

Google Scholar

[8] I. Rivals, L. Personnaz : Nonlinear internal model control using neural networks: Application to processes with delay and design issues, IEEE Transactions on Neural Networks, Vol. 11, p.80–90 (2000).

DOI: 10.1109/72.822512

Google Scholar

[9] R. Mendes, J. Kennedy, J. Neves : The fully informed particle swarm: simpler, maybe better, IEEE Transaction on Evolutionary Computation, vol. 8, p.204–210 (2004).

DOI: 10.1109/tevc.2004.826074

Google Scholar