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Design of Robust Controller for an Uncertain System described by Unstructured Uncertainty Model using Small Gain Theorem

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

    Abstract

    A simple and easy technique is used to design a robust Proportional Integral Derivative (PID)/ Proportional Integral (PI) controller for an uncertain system described by unstructured uncertainty model. The controller design is based on Small Gain theorem and it guarantees closed loop stability for input variations across its full working range. The uncertain plant under different working ranges is described by a family of corresponding transfer functions. In this paper design of a robust controller has been demonstrated for a warm air-drying chamber. Closed loop step response and frequency response plots are shown to prove that the proposed controller provides robust operation of the chosen uncertain plant. Comparison of the results with those obtained from other design methods shows that the proposed controller design methodology proves to be a better option.

    Original languageEnglish
    Title of host publication2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages459-463
    Number of pages5
    ISBN (Electronic)9781538680100
    DOIs
    Publication statusPublished - 01-04-2019
    Event2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019 - London, United Kingdom
    Duration: 24-04-201926-04-2019

    Publication series

    Name2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

    Conference

    Conference2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019
    Country/TerritoryUnited Kingdom
    CityLondon
    Period24-04-1926-04-19

    All Science Journal Classification (ASJC) codes

    • Strategy and Management
    • Artificial Intelligence
    • Management of Technology and Innovation
    • Computer Science Applications
    • Information Systems and Management
    • Control and Optimization
    • Instrumentation

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