Article

Fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization

Authors:

Ricardo Martinez-Soto,

Oscar Castillo,

Luis T. Aguilar,

Patricia MelinAuthors Info & Claims

MICAI'10: Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II

Pages 475 - 486

Published: 08 November 2010 Publication History

Abstract

In this paper we apply to Bio-inspired and evolutionary optimization methods to design fuzzy logic controllers (FLC) to minimize the steady state error of linear systems. We test the optimal FLC obtained by the genetic algorithms and the PSO applied on linear systems using benchmark plants. The bioinspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results are obtained with Simulink showing the feasibility of the proposed approach.

References

[1]

Engelbretht, A.P.: Fundamentals of Computational Swarm Intelligence, pp. 5-129. John Wiley & Sons Ltd., England (2005).

Digital Library

[2]

Melin, P., Castillo, O.: A New Method for Adaptive Control of Non-Linear Plants Using Type-2 Fuzzy Logic and Neural Networks. International Journal of General Systems 1563- 5104 33(2), 289-304 (2004).

[3]

Sepúlveda, R., Montiel, O., Castillo, O., Melin, P.: Optimizing the MFs in Type-2 Fuzzy Logic Controllers. Using the Human Evolutionary Model International Review of Automatic Control (IREACO) Theory and Applications 3(1), 1-10 (2010).

[4]

Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39-43 (1995); Lu, J.-G.: Title of paper with only the first word capitalized. J. Name Stand. Abbrev (in press).

[5]

Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks 5(1), 3-14 (1994).

Digital Library

[6]

Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, Anchorage, Alaska, IEEE, Los Alamitos (1998).

[7]

Kennedy, J., Mendes, R.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58-73 (2002).

Digital Library

[8]

Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceeding of IEEE Conference on Evolutionary Computation, pp. 1671-1676 (2002).

Digital Library

[9]

Angeline, P.J.: Evolutionary Optimization versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601-610. Springer, Heidelberg (1998).

Digital Library

[10]

Alcalá, R., Alcalá-Fdez, J., Herrera, F.: A proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and its Interaction with Rule Selection. IEEE Transactions on Fuzzy Systems 15(4), 616-635 (2007).

Digital Library

[11]

Alcalá, R., Gacto, M.J., Herrera, F., Alcalá-Fdez, J.: A Multi-objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15(5), 539-557 (2007).

[12]

Casillas, J., Cordon, O., del Jesús, M.J., Herrera, F.: Genetic Tuning of Fuzzy Rule Deep Structures Preserving Interpretability and its Interaction with Fuzzy Rule Set Reduction. IEEE Transaction on Fuzzy Systems 13(1), 13-29 (2005).

Digital Library

[13]

Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten Years of Genetic Fuzzy Systems: Current Framework and New trends. Fuzzy Sets and Systems 141(1), 5- 31 (2004).

[14]

Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms: With Applications to Image Processing and Pattern recognition. World Scientific, Singapore (1996).

Digital Library

[15]

Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control. Springer, Berlin (1993).

Digital Library

[16]

Fukao, T., Nakagawa, H., Adachi, N.: Adaptive Tracking Control of a NonHolonomic Mobile Robot. IEEE Trans. On Robotics and Automation 16(5), 609-615 (2000).

[17]

Liang, Q., Mendel, J.M.: Interval Type-2 Fuzzy Logic Systems: Theory and Design. IEEE Trans. on Fuzzy Systems 8(5), 535-550 (2000).

Digital Library

[18]

Lee, T.H., Leung, F.H.F., Tam, P.K.S.: Position Control for Wheeled Mobile Robot Using a Fuzzy Controller, pp. 525-528. IEEE, Los Alamitos (1999).

[19]

Pedrycz, W. (ed.): Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Press, Dordrecht (1996).

Digital Library

[20]

Martinez, R., Castillo, O., Aguilar, L.T., Rodriguez, A.: Evolutionary Optimization of type-2 Fuzzy Systems Applied to Linear Plants. To appear in System, Man, and Cybernetic Conference (2009).

[21]

Martinez, R., Castillo, O., Aguilar, L.T.: Intelligent Control For A Perturbed Autonomous Wheeled Mobile Robot Using Type-2 Fuzzy Logic and Genetic Algorithms. Journal of Automation, Mobile Robotics & Intelligent Systems 2 (2008); Wilkinson, J.P.: Nonlinear resonant circuit devices (Patent style), U.S. Patent 3 624 12 (July 16, 1990).

Cited By

Banu SBaskaran K(2018)Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area NetworksWireless Personal Communications: An International Journal10.1007/s11277-018-5626-4100:3(1163-1199)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11277-018-5626-4
Nekooei SChen GRayudu R(2017)Automatic design of fuzzy logic controllers for medium access control in wireless body area networks An evolutionary approachApplied Soft Computing10.1016/j.asoc.2017.02.02256:C(245-261)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.02.022
Yusupbekov NMarakhimov AIgamberdiev HUmarov S(2016)Application of Soft-computing Technologies to the Traffic Control System Design ProblemsProcedia Computer Science10.1016/j.procs.2016.09.440102:C(540-546)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.09.440

Fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization

Recommendations

Type-2 Fuzzy Logic Controllers Optimization Using Genetic Algoritms and Particle Swarm Optimization
GRC '10: Proceedings of the 2010 IEEE International Conference on Granular Computing

In this paper we apply bio-inspired optimization methods to design type-2 fuzzy logic controllers (FLC) to minimize the steady state error of linear systems. We test the optimal FLC obtained by the genetic algorithms and the PSO using benchmark plants. ...
A fuzzy adaptive turbulent particle swarm optimisation

Particle Swarm Optimisation (PSO) algorithm is a stochastic search technique, which has exhibited good performance across a wide range of applications. However, very often for multimodal problems involving high dimensions, the algorithm tends to suffer ...
High-Dimension optimization problems using specified particle swarm optimization
ICSI'12: Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I

Particle Swarm Optimization (PSO), proposed by Dr. J. Kennedy and Professor R. Eberhart in 1995, attracts many attentions to solve for a lot of real uni-modal/multi-modal optimization problems. Due to its simplicity of parametersetting and computational ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

MICAI'10: Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II

November 2010

514 pages

ISBN:3642167721

Editors:
Grigori Sidorov
Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, México
,
Arturo Hernández Aguirre
Centro de Investigación en Matemáticas, Departamento de Ciencias de la Computación, Guanajuato, Guanajuato, México
,
Carlos Alberto Reyes García
Instituto Nacional de Astrofísica, Optica y Electrónica, Coordinación de Ciencias Computacionales, Puebla, México

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 November 2010

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Banu SBaskaran K(2018)Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area NetworksWireless Personal Communications: An International Journal10.1007/s11277-018-5626-4100:3(1163-1199)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11277-018-5626-4
Nekooei SChen GRayudu R(2017)Automatic design of fuzzy logic controllers for medium access control in wireless body area networks An evolutionary approachApplied Soft Computing10.1016/j.asoc.2017.02.02256:C(245-261)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.02.022
Yusupbekov NMarakhimov AIgamberdiev HUmarov S(2016)Application of Soft-computing Technologies to the Traffic Control System Design ProblemsProcedia Computer Science10.1016/j.procs.2016.09.440102:C(540-546)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.09.440

View Options

View options

Media

Figures

Other

Tables

View Table of Contents