Dual adaptive control of nonlinear stochastic systems

Randa Herzallah*

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    The paper proposes a dual adaptive control scheme for general class of nonlinear stochastic discrete control systems. The method is a simpler alternative to the well known stochastic adaptive control techniques which is known to be computationally expensive. It is developed under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. The proposed scheme avoids the pre-control neural network training phase by taking into consideration models uncertainty and its effect on tracking, in the on-line control phase. The implementation of this scheme requires finding a suitable method for estimating models uncertainty. For illustration purposes the proposed scheme is applied to linear stochastic systems and the desired results are obtained. A simulation example is also worked out in the paper to demonstrate efficiency of the proposed method.

    Original languageEnglish
    Title of host publicationWMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
    Pages163-168
    Number of pages6
    Publication statusPublished - 2005
    Event9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005 - Orlando, FL, United States
    Duration: 10 Jul 200513 Jul 2005

    Publication series

    NameWMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
    Volume2

    Conference

    Conference9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005
    Country/TerritoryUnited States
    CityOrlando, FL
    Period10/07/0513/07/05

    Keywords

    • Dual adaptive control
    • Neural networks
    • Nonlinear stochastic systems
    • Uncertainty

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