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<p>Aiming to achieve motion-force control and solve uncertainties in the kinematic control of mobile manipulators, this paper proposes an admittance-based learning and control (ALC) scheme for mobile manipulators with unknown kinematic parameters. Initially, the motion-force control problem of maintaining a desired force between the robot and the contact object is investigated based on the idea of admittance control. Subsequently, a learning formula for estimating the holistic Jacobian matrix is devised. Further, the scheme is formulated as a quadratic programming (QP) problem solved by the devised neural dynamics controller to achieve simultaneous learning and control of mobile manipulators. Theoretical analyses prove the learning and control convergences of the devised neural dynamics controller. Simulations indicate the learning error is kept at the order of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(10^{-3}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>3</mn> </mrow> </msup> </math></EquationSource> </InlineEquation> and the control error is kept at the order of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(10^{-4}~\text {m}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>4</mn> </mrow> </msup> <mspace width="3.33333pt" /> <mtext>m</mtext> </mrow> </math></EquationSource> </InlineEquation>. The effectiveness of the scheme is further validated through various physical experiments.</p>

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Admittance-based learning and control for mobile manipulators based on neural dynamics

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Zhaoyuan Zhang,
  • Jialiang Fan

摘要

Aiming to achieve motion-force control and solve uncertainties in the kinematic control of mobile manipulators, this paper proposes an admittance-based learning and control (ALC) scheme for mobile manipulators with unknown kinematic parameters. Initially, the motion-force control problem of maintaining a desired force between the robot and the contact object is investigated based on the idea of admittance control. Subsequently, a learning formula for estimating the holistic Jacobian matrix is devised. Further, the scheme is formulated as a quadratic programming (QP) problem solved by the devised neural dynamics controller to achieve simultaneous learning and control of mobile manipulators. Theoretical analyses prove the learning and control convergences of the devised neural dynamics controller. Simulations indicate the learning error is kept at the order of \(10^{-3}\) 10 - 3 and the control error is kept at the order of \(10^{-4}~\text {m}\) 10 - 4 m . The effectiveness of the scheme is further validated through various physical experiments.