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Neuro-fuzzy control of electroactive polymer actuators

Conference Paper


Abstract


  • Electroactive polymer actuators, especially those

    based on polypyrrole (PPy), possess unique characteristics such

    as an ability to operate at the macro or micro scale, large forceto-

    weight ratio, biocompatibility, low cost and operation in

    aqueous and non-aqueous environments. Therefore, they are

    very suitable for the establishment of bio-mimetic devices,

    single-cell manipulators, robotics, prosthetics, and numerous

    biomedical applications. In this paper, we report on the neurofuzzy

    control of these actuators, which are typified by the trilayer

    polypyrrole actuators considered in this paper, in order

    to improve their positioning accuracy and speed of response.

    We experimentally evaluated two model-free intelligent control

    strategies, which are fuzzy logic PD+I control and neuro-fuzzy

    Adaptive Neural Fuzzy Inference System (ANFIS) control. The

    performance of these intelligent controllers is compared to that

    of a conventional Proportional Integral Derivative (PID)

    controller. The experimental results demonstrated that they

    significantly outperformed the conventional PID controller

    with an improvement in rise time of at least 18 times and in

    settling time of at least 2 times. To the best of authors’

    knowledge, this is the first study to design and evaluate fuzzy

    logic PD+I and neuro-fuzzy ANFIS PD+I intelligent control

    methodologies for an important class of electroactive polymer

    actuators.

UOW Authors


Publication Date


  • 2013

Citation


  • Druitt, C. M. & Alici, G. (2013). Neuro-fuzzy control of electroactive polymer actuators. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 373-380). United States: IEEE.

Scopus Eid


  • 2-s2.0-84883704605

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1248

Has Global Citation Frequency


Start Page


  • 373

End Page


  • 380

Place Of Publication


  • United States

Abstract


  • Electroactive polymer actuators, especially those

    based on polypyrrole (PPy), possess unique characteristics such

    as an ability to operate at the macro or micro scale, large forceto-

    weight ratio, biocompatibility, low cost and operation in

    aqueous and non-aqueous environments. Therefore, they are

    very suitable for the establishment of bio-mimetic devices,

    single-cell manipulators, robotics, prosthetics, and numerous

    biomedical applications. In this paper, we report on the neurofuzzy

    control of these actuators, which are typified by the trilayer

    polypyrrole actuators considered in this paper, in order

    to improve their positioning accuracy and speed of response.

    We experimentally evaluated two model-free intelligent control

    strategies, which are fuzzy logic PD+I control and neuro-fuzzy

    Adaptive Neural Fuzzy Inference System (ANFIS) control. The

    performance of these intelligent controllers is compared to that

    of a conventional Proportional Integral Derivative (PID)

    controller. The experimental results demonstrated that they

    significantly outperformed the conventional PID controller

    with an improvement in rise time of at least 18 times and in

    settling time of at least 2 times. To the best of authors’

    knowledge, this is the first study to design and evaluate fuzzy

    logic PD+I and neuro-fuzzy ANFIS PD+I intelligent control

    methodologies for an important class of electroactive polymer

    actuators.

UOW Authors


Publication Date


  • 2013

Citation


  • Druitt, C. M. & Alici, G. (2013). Neuro-fuzzy control of electroactive polymer actuators. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 373-380). United States: IEEE.

Scopus Eid


  • 2-s2.0-84883704605

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1248

Has Global Citation Frequency


Start Page


  • 373

End Page


  • 380

Place Of Publication


  • United States