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Adaptive control of rigid-link electrically driven robots with parametric uncertainties in kinematics and dynamics and without acceleration measurements.

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Title: Adaptive control of rigid-link electrically driven robots with parametric uncertainties in kinematics and dynamics and without acceleration measurements.
Authors: Ahmadipour, Mahboubeh1, Khayatian, Alireza1, Dehghani, Maryam1
Source: Robotica. Oct2014, Vol. 32 Issue 7, p1153-1169. 17p.
Subjects: Robot kinematics, Tracking control systems, Robot dynamics, Robot control systems, Global asymptotic stability, Stochastic convergence, Adaptive control systems
Abstract: In this paper, the backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of parametric uncertainties in kinematics, manipulator dynamics, and actuator dynamics. To avoid acceleration measurements, two techniques are exploited. One technique adds compensation control terms to the control law signal. The other uses a linear in variable property of the Jacobian matrix. Global asymptotic convergence of the end-effector motion tracking errors is shown via Lyapunov analysis. Simulation results are presented to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
Copyright of Robotica is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 97518771
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: In this paper, the backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of parametric uncertainties in kinematics, manipulator dynamics, and actuator dynamics. To avoid acceleration measurements, two techniques are exploited. One technique adds compensation control terms to the control law signal. The other uses a linear in variable property of the Jacobian matrix. Global asymptotic convergence of the end-effector motion tracking errors is shown via Lyapunov analysis. Simulation results are presented to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Robotica is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S0263574713001203
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 1153
    Subjects:
      – SubjectFull: Robot kinematics
        Type: general
      – SubjectFull: Tracking control systems
        Type: general
      – SubjectFull: Robot dynamics
        Type: general
      – SubjectFull: Robot control systems
        Type: general
      – SubjectFull: Global asymptotic stability
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      – SubjectFull: Stochastic convergence
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
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      – TitleFull: Adaptive control of rigid-link electrically driven robots with parametric uncertainties in kinematics and dynamics and without acceleration measurements.
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              M: 10
              Text: Oct2014
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              Y: 2014
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