Time-varying force tracking in impedance control a case study for automatic cell manipulation

Wang, Xu; Cai, Chenxiao; Zou, Yun

doi:10.1109/icca.2013.6564970

Cited by 2 publications

(1 citation statement)

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“…However, the contact force overshoot, which generated from the contact between robot and environment, was usually unconcerned in the above methods design. An improved impedance relationship was proposed in [26,27] to reduce the contact force overshoot and to achieve the direct accurate time-varying force tracking. Roveda et al [28,29] calculated the parameters online in external controller to reduce the contact force overshoot.…”

Section: Introductionmentioning

confidence: 99%

Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Peng

Yang

2019

Complexity

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In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

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