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Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning

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Complex System Modelling and Control Through Intelligent Soft Computations

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

Abstract

In this study, a new advanced metaheuristics-based optimization approach is proposed and successfully applied to design and tuning of a PID-type Fuzzy Logic Controller (FLC). The scaling factors tuning problem of the FLC structure is formulated and systematically resolved, using various constrained metaheuristics such as the Differential Search Algorithm (DSA), Gravitational Search Algorithm (GSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). In order to specify more time-domain performance control objectives of the proposed metaheuristics-tuned PID-type FLC, different optimization criteria such as Integral of Square Error (ISE) and Maximum Overshoot (MO) are considered and compared The classical Genetic Algorithm Optimization (GAO) method is also used as a reference tool to measure the statistical performances of the proposed methods. All these algorithms are implemented and analyzed in order to show the superiority and the effectiveness of the proposed fuzzy control tuning approach. Simulation and real-time experimental results, for an electrical DC drive benchmark, show the advantages of the proposed metaheuristics-tuned PID-type fuzzy control structure in terms of performance and robustness.

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References

  • Azar, A. T. (Ed.) (2010a). Fuzzy systems. Vienna, Austria: INTECH. ISBN 978-953-7619-92-3.

    Google Scholar 

  • Azar, A. T. (2010b). Adaptive neuro-fuzzy systems. In Fuzzy systems. Vienna, Austria: INTECH. ISBN 978-953-7619-92-3.

    Google Scholar 

  • Azar, A. T. (2012). Overview of type-2 fuzzy logic systems. International Journal of Fuzzy System Applications, 2(4), 1–28.

    Google Scholar 

  • Azar, A. T., & Serrano, F. E. (2014). Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications. Neural Computing and Applications,. doi:10.1007/s00521-014-1560-x.

    Google Scholar 

  • Basturk, B. & Karaboga, D. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization. In Proceedings of IEEE Swarm Intelligence Symposium, May 12–14, Indianapolis, USA.

    Google Scholar 

  • Bouallègue, S., Haggège, J., Ayadi, M., & Benrejeb, M. (2012a). PID-type fuzzy logic controller tuning based on particle swarm optimization. Engineering Applications of Artificial Intelligence, 25(3), 484–493.

    Article  Google Scholar 

  • Bouallègue, S., Haggège, J., & Benrejeb, M. (2011). Particle swarm optimization-based fixed-structure \(\mathcal{H}_{\infty}\)control design. International Journal of Control, Automation and Systems, 9(2), 258–266.

    Article  Google Scholar 

  • Bouallègue, S., Haggège, J., & Benrejeb, M. (2012b). A new method for tuning PID-type fuzzy controllers using particle swarm optimization. In Fuzzy Controllers: Recent Advances in Theory and Applications (pp. 139–162). Vienna, Austria: INTECH. ISBN 978-953-51-0759-0.

    Google Scholar 

  • Boussaid, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237(1), 82–117.

    Article  MathSciNet  Google Scholar 

  • Civicioglu, P. (2012). Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers and Geosciences, 46(1), 229–247.

    Article  Google Scholar 

  • David, R. C., Precup, R. E., Petriu, E. M., Radac, M. B., & Preitl, S. (2013). Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity. Information Sciences, 247(1), 154–173.

    Article  MathSciNet  Google Scholar 

  • Dréo, J., Siarry, P., Pétrowski, A., & Taillard, E. (2006). Metaheuristics for Hard Optimization Methods and Case Studies. Heidelberg: Springer.

    Google Scholar 

  • Eberhart, R. & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micro Machine and Human Science (pp. 39–43), October 4–6, Nagoya, Japan.

    Google Scholar 

  • Eker, I., & Torun, Y. (2006). Fuzzy logic control to be conventional method. Energy Conversion and Management, 47(4), 377–394.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley Publishing Company.

    Google Scholar 

  • Goswami, D., & Chakraborty, S. (2014). Differential search algorithm-based parametric optimization of electrochemical micromachining processes. International Journal of Industrial Engineering Computations, 5(1), 41–54.

    Article  Google Scholar 

  • Guzelkaya, M., Eksin, I., & Yesil, E. (2003). Self-tuning of PID type fuzzy logic controller coefficients via relative rate observer. Engineering Applications of Artificial Intelligence, 16(3), 227–236.

    Article  Google Scholar 

  • Haggège, J., Ayadi, M., Bouallègue, S., & Benrejeb, M. (2010). Design of Fuzzy Flatness-based Controller for a DC Drive. Control and Intelligent Systems, 38(3), 164–172.

    Article  MATH  Google Scholar 

  • Haggège, J., Bouallègue, S., & Benrejeb, M. (2009). Robust \(\mathcal{H}_{\infty}\)Design for a DC Drive. International Review of Automatic Control, 2(4), 415–422.

    Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey.

    Google Scholar 

  • Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.

    Article  MATH  MathSciNet  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.

    Article  MATH  MathSciNet  Google Scholar 

  • Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697.

    Article  Google Scholar 

  • Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2012). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligent Review, 42, 21–57. doi:10.1007/s10462-012-9328-0.

    Article  Google Scholar 

  • Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings. of IEEE International Joint Conference on Neural Networks (pp. 1942–1948), November 27–December 01, Perth, Australia.

    Google Scholar 

  • Lee, C. C. (1998a). Fuzzy logic in control systems: Fuzzy logic controller-part I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404–418.

    Article  Google Scholar 

  • Lee, C. C. (1998b). Fuzzy logic in control systems: Fuzzy logic controller-part II. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 419–435.

    Article  Google Scholar 

  • Madiouni, R., Bouallègue, S., Haggège, J., & Siarry, P. (2013). Particle swarm optimization-based design of polynomial RST controllers. In Proceedings. of the 10th IEEE International Multi-Conference on Systems, Signals and Devices (pp. 1–7), Hammamet, Tunisia.

    Google Scholar 

  • MathWorks. (2009). Genetic algorithm and direct search toolbox user’s guide.

    Google Scholar 

  • Nobahari, H., Nikusokhan, M., & Siarry, P. (2011). Non-dominated sorting gravitational search algorithm. In Proceedings. of the International conference on swarm intelligence (pp. 1–10), June 14–15, Cergy, France.

    Google Scholar 

  • Passino, K. M. & Yurkovich, S. (1998). Fuzzy control. Boston, Addison Wesley Longman.

    Google Scholar 

  • Precup, R. E., David, R. C., Petriu, E. M., Preitl, S., & Radac, M. B. (2011). Gravitational search algorithms in fuzzy control systems tuning. In Proceedings. of the 18th IFAC World Congress (pp. 13624–13629), August 28–September 02, Milano, Italy.

    Google Scholar 

  • Qiao, W. Z., & Mizumoto, M. (1996). PID type fuzzy controller and parameters adaptive method. Fuzzy Sets and Systems, 78(1), 23–35.

    Article  MATH  MathSciNet  Google Scholar 

  • Rao, R. V. and Savsani, V. J. (2012). Mechanical design optimization using advanced optimization techniques. Heidelberg: Springer.

    Google Scholar 

  • Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.

    Article  MATH  Google Scholar 

  • Siarry, P., & Michalewicz, Z. (2008). Advances in metaheuristics for hard optimization. New York: Springer.

    Book  MATH  Google Scholar 

  • Toumi, F., Bouallègue, S., Haggège, J., & Siarry, P. (2014). Differential search algorithm-based approach for PID-type fuzzy controller tuning. In Proceedings. of the International Conference on Control, Engineering & Information Technology, March 22–25, Sousse, Tunisia.

    Google Scholar 

  • Waghole, V., & Tiwari, R. (2014). Optimization of needle roller bearing design using novel hybrid methods. Mechanism and Machine Theory, 72(1), 71–85.

    Article  Google Scholar 

  • Woo, Z. W., Chung, H. Y., & Lin, J. J. (2000). A PID type fuzzy controller with self-tuning scaling factors. Fuzzy Sets and Systems, 115(2), 321–326.

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Research Laboratory in Automatic Control LA.R.A, National Engineering School of Tunis (ENIT), BP 37, Le Belvdre, 1002, Tunis, Tunisia

    Soufiene Bouallègue, Fatma Toumi & Joseph Haggège

  2. Signals, Images and Intelligent Systems Laboratory, LiSSi-EA-3956, University Paris-Est Créteil Val de Marne, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Fatma Toumi & Patrick Siarry

Authors
  1. Soufiene Bouallègue
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  2. Fatma Toumi
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  3. Joseph Haggège
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  4. Patrick Siarry
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Corresponding author

Correspondence to Fatma Toumi .

Editor information

Editors and Affiliations

  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Bouallègue, S., Toumi, F., Haggège, J., Siarry, P. (2015). Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-12883-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12882-5

  • Online ISBN: 978-3-319-12883-2

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