A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

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Mostafa Bagheri, Iasson Karafyllis, Peiman Naseradinmousavi and Miroslav Krstić, "Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 86-93, Jan. 2021. doi: 10.1109/JAS.2020.1003459
Citation: Mostafa Bagheri, Iasson Karafyllis, Peiman Naseradinmousavi and Miroslav Krstić, "Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 86-93, Jan. 2021. doi: 10.1109/JAS.2020.1003459

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

doi: 10.1109/JAS.2020.1003459
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  • We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters’ estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated.

     

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    Highlights

    • First application in robotics of regulation-triggered adaptive control with batch least-squares (BaLSI) identification.
    • Performs piecewise-constant, rather than continuous, updates of parameter estimates.
    • Parameter estimation settles in finite time and reaches the true unknown parameters, even in the absence of persistency of excitation.
    • After a finite learning transient, recovers the performance of the non-adaptive design, including exponential regulation.

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