Design of Gain Scheduled Multivariable Controllers for a Nonlinear Process

N. Kamala; T. Thyagarajan; S. Renganathan

doi:10.4028/www.scientific.net/AMR.403-408.4821

Paper Titles

Fuzzy Controller for Positioning Tasks in Tactile Surgical Navigation
p.4794

Digital Controller Design for Half-Car Active Suspension System with Using Singular Perturbation Theory
p.4800

Anti-Control of Chaos of Linear Continuous-Time Systems with Unknown Parameters Using Adaptive Control
p.4806

Gain and Order Scheduling of Optimal Fractional Order PID Controllers for Random Delay and Packet Dropout in Networked Control Systems
p.4814

Design of Gain Scheduled Multivariable Controllers for a Nonlinear Process
p.4821

Speed Control of Hydrulic Motor System with Swashplate DC-Controlled Pump
p.4828

Control of D.C Motor by Computer
p.4841

A Novel Fuzzy Adaptive Speed Estimator for Space Vector Modulated DTFC of AC Drives
p.4850

Generalized Frequency Domain Robust Tuning of a Family of Fractional Order PI/PID Controllers to Handle Higher Order Process Dynamics
p.4859

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Design of Gain Scheduled Multivariable Controllers...

Design of Gain Scheduled Multivariable Controllers for a Nonlinear Process

Abstract:

In this paper, Genetic Algorithm is utilized to optimize the coefficients of a decentralized PID controller for a nonlinear Multi-Input Multi-output process by minimizing the Integral Absolute Error (IAE).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

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

4821-4827

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.4821

Citation:

Cite this paper

Online since:

November 2011

Authors:

N. Kamala, T. Thyagarajan, S. Renganathan

Keywords:

CSTR, Decentralized Control, Fuzzy, GA, IAE, PID

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Qiang Xiong, Wen-Jian Cai, Ming He, A practical Decentralized 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).

Google Scholar

[2] J. Lee, T. Edgar, Interaction measure for decentralized control of multivariable process, Proc of American Control Conference, Anchorage, AK, 2002, pp.454-458.

Google Scholar

[3] Blanchett, T.P., Kember.G. C, Dubey. R, 2000, PID Gain Scheduling using Fuzzy Logic, ISA Transactions, Vol. 39, pp.317-325.

DOI: 10.1016/s0019-0578(00)00024-0

Google Scholar

[4] Wah. B, and Chen . Y, Constrained Genetic Algorithms and their applications in nonlinear Constrained Optimization, Proc. Of International Conference on Tools with Artificial Intelligence, IEEE, November, pp.286-293 (2000).

DOI: 10.1109/tai.2000.889884

Google Scholar

[5] Sadasivarao M.V. and Chidambaram,M., PID Controller tuning of cascade control systems using Genetic Algorithm, Journal of Indian Institute of Science, 86, July-August, pp.343-354 (2006).

Google Scholar

[6] Ogunnaike, B. A, and Ray, W.H.: Process Dynamics, Modeling and Control, Oxford University Press, New York, (1994).

Google Scholar

[7] M. Pottman and D.E. Seborg, Identification of nonlinear process using Reciprocal Multi Quadratic Functions, Journal of Process Control, 2, 1992, pp.189-203.

DOI: 10.1016/0959-1524(92)80008-l

Google Scholar

[8] T. Johansen,R. Murray-Smith, Multiple model approaches to modeling and control, Taylor and Francis Limited, London, UK, (1997).

Google Scholar