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Multifactor optimization of a Fuzzy-PID controller using genetic algorithm

Journal Article


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Abstract


  • The design of a Fuzzy-PID controller involves setting the fuzzy rules, membership

    functions and its associated scaling factors. How to obtain a better control result and how these

    scaling factors affect the controller’s performance are still a challenge. In this paper, the automatic

    position control system of a Hille 100 experimental rolling mill was used as a research testbed. Based

    on the mathematical control model of the rolling mill, a Fuzzy-PID controller was developed, and the

    process of implementing global optimization considering all these factors simultaneously by using

    genetic algorithm is introduced in detail. Through simulation, the performance of the control system

    with multifactor optimized Fuzzy-PID controller is given, and compared with that with only the fuzzy

    rules optimized in the controller. By simulation tests, it is found that these factors will influence the

    control performance of the controller, and that they are highly coupled with each other. The more

    factors for a Fuzzy-PID controller are optimized, the better the solution will be. It can also be inferred

    from the study that asymmetrical membership functions have more potential in improving a fuzzy

    controller’s performance than symmetrical ones. The multifactor optimization method presented in

    this paper can in principle also be used to solve other complicated optimization issues.

Publication Date


  • 2012

Citation


  • He, X., Chen, W., Zhu, B., Jiang, Z. & Cook, C. David. (2012). Multifactor optimization of a Fuzzy-PID controller using genetic algorithm. Advanced Materials Research, 422 268-275.

Scopus Eid


  • 2-s2.0-84255192127

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1037&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/32

Number Of Pages


  • 7

Start Page


  • 268

End Page


  • 275

Volume


  • 422

Abstract


  • The design of a Fuzzy-PID controller involves setting the fuzzy rules, membership

    functions and its associated scaling factors. How to obtain a better control result and how these

    scaling factors affect the controller’s performance are still a challenge. In this paper, the automatic

    position control system of a Hille 100 experimental rolling mill was used as a research testbed. Based

    on the mathematical control model of the rolling mill, a Fuzzy-PID controller was developed, and the

    process of implementing global optimization considering all these factors simultaneously by using

    genetic algorithm is introduced in detail. Through simulation, the performance of the control system

    with multifactor optimized Fuzzy-PID controller is given, and compared with that with only the fuzzy

    rules optimized in the controller. By simulation tests, it is found that these factors will influence the

    control performance of the controller, and that they are highly coupled with each other. The more

    factors for a Fuzzy-PID controller are optimized, the better the solution will be. It can also be inferred

    from the study that asymmetrical membership functions have more potential in improving a fuzzy

    controller’s performance than symmetrical ones. The multifactor optimization method presented in

    this paper can in principle also be used to solve other complicated optimization issues.

Publication Date


  • 2012

Citation


  • He, X., Chen, W., Zhu, B., Jiang, Z. & Cook, C. David. (2012). Multifactor optimization of a Fuzzy-PID controller using genetic algorithm. Advanced Materials Research, 422 268-275.

Scopus Eid


  • 2-s2.0-84255192127

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1037&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/32

Number Of Pages


  • 7

Start Page


  • 268

End Page


  • 275

Volume


  • 422