Model reference control using CMAC neural networks

Alpaslan Duysak, Abdurrahman Unsal, Jeffrey Louis Schiano

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.

    Original languageEnglish (US)
    Title of host publicationArtificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
    Pages670-679
    Number of pages10
    EditionPART 2
    StatePublished - Dec 1 2007
    Event17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal
    Duration: Sep 9 2007Sep 13 2007

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume4669 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other17th International Conference on Artificial Neural Networks, ICANN 2007
    CountryPortugal
    CityPorto
    Period9/9/079/13/07

    Fingerprint

    Reference Model
    Neural Networks
    Neural networks
    Real-world Applications
    System Identification
    Testbed
    Nonlinear Model
    Dynamic Model
    Nonlinear Systems
    Disturbance
    Control System
    Testbeds
    Fluid
    Nonlinear systems
    Output
    Identification (control systems)
    Experimental Results
    Demonstrate
    Control systems
    Fluids

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Duysak, A., Unsal, A., & Schiano, J. L. (2007). Model reference control using CMAC neural networks. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings (PART 2 ed., pp. 670-679). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4669 LNCS, No. PART 2).
    Duysak, Alpaslan ; Unsal, Abdurrahman ; Schiano, Jeffrey Louis. / Model reference control using CMAC neural networks. Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2. ed. 2007. pp. 670-679 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
    @inproceedings{37e7d9739c924d138e24471f7d346b7c,
    title = "Model reference control using CMAC neural networks",
    abstract = "This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.",
    author = "Alpaslan Duysak and Abdurrahman Unsal and Schiano, {Jeffrey Louis}",
    year = "2007",
    month = "12",
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    Duysak, A, Unsal, A & Schiano, JL 2007, Model reference control using CMAC neural networks. in Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4669 LNCS, pp. 670-679, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, 9/9/07.

    Model reference control using CMAC neural networks. / Duysak, Alpaslan; Unsal, Abdurrahman; Schiano, Jeffrey Louis.

    Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2. ed. 2007. p. 670-679 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4669 LNCS, No. PART 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AU - Unsal, Abdurrahman

    AU - Schiano, Jeffrey Louis

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    N2 - This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.

    AB - This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.

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    Duysak A, Unsal A, Schiano JL. Model reference control using CMAC neural networks. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 2 ed. 2007. p. 670-679. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).