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Identification, control and robustness analysis of a robotic system using fractional control

    Research output: Contribution to scientific journalArticle in an indexed scientific journalpeer-review

    25 Scopus citations

    Abstract

    This paper presents the identification, control and robustness analysis for a robotic system of 1 degree of freedom using fractional and integer PID controllers. Initially, an ADAMS-MATLAB co-simulation model is built to simulate the dynamic model of robotic system. The identification of the dynamic model parameters of the system employs the recursive least-squares method. The controller design uses a linearized model of the robotic system obtained by the method of input-output linearization, and then, the fractional PID and integer PID controllers are designed based on it. The robustness of the PID controllers is analyzed in the frequency and the time domain. The robustness analysis in the frequency domain uses The Gang of Four (sensibility, complementary sensibility, sensibility of the plant and sensibility of the controller effort). The time domain analysis uses the ADAMS-MATLAB co-simulation model, considering external disturbances, noise in the feedback loop, parametric uncertainty and set-point variation, using as performance criteria ISE, average value and standard deviation of the control action. The tests are performed for positioning and tracking tasks. The obtained results show that the fractional PID controller has a better behavior in presence of external disturbances and lower energy consumption.

    Original languageEnglish
    Pages (from-to)1294-1302
    Number of pages9
    JournalIEEE Latin America Transactions
    Volume13
    Issue number5
    DOIs
    StatePublished - 1 May 2015

    Bibliographical note

    Publisher Copyright:
    © 2015 IEEE.

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    Keywords

    • ADAMS-MATLAB
    • FOPID
    • IOPID
    • co-simulation
    • identification
    • input-output linearization
    • robotics
    • robustness analysis

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