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<p>This paper presents the modeling and control of a piezoelectric robotic manipulator designed to characterize the behavior of a deformable object exhibiting nonlinear and viscoelastic deformation. The manipulator’s nonlinear dynamics are approximated using a classical Bouc-Wen hysteresis model, which captures its inherent hysteretic behavior. An output feedback control law is developed to ensure accurate position reference tracking for the manipulator. The control strategy leverages state observers to estimate both the manipulator’s states and the interaction force with the deformable object. By combining the estimated interaction force with the controlled position data, the force-deformation characteristics of the object are analyzed, providing insight into its behavior and enabling the potential identification of its model parameters. The robustness of the proposed approach is demonstrated through simulations and experiments, where Gaussian noise was added to the system to evaluate its performance under realistic conditions.</p>

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Nonlinear displacement control and force estimation in a piezoelectric robotic manipulator characterizing an object with nonlinear viscoelastic deformation

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • Gerardo Flores,
  • Micky Rakotondrabe

摘要

This paper presents the modeling and control of a piezoelectric robotic manipulator designed to characterize the behavior of a deformable object exhibiting nonlinear and viscoelastic deformation. The manipulator’s nonlinear dynamics are approximated using a classical Bouc-Wen hysteresis model, which captures its inherent hysteretic behavior. An output feedback control law is developed to ensure accurate position reference tracking for the manipulator. The control strategy leverages state observers to estimate both the manipulator’s states and the interaction force with the deformable object. By combining the estimated interaction force with the controlled position data, the force-deformation characteristics of the object are analyzed, providing insight into its behavior and enabling the potential identification of its model parameters. The robustness of the proposed approach is demonstrated through simulations and experiments, where Gaussian noise was added to the system to evaluate its performance under realistic conditions.