Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6867249 | Robotics and Autonomous Systems | 2018 | 12 Pages |
Abstract
The traditional constant impedance control is a simple but effective method widely used in many fields including contact force tracking. Using this method, the location of the environment relative to the robot and the stiffness of the environment must be known, and usually the desired force is constant. However, for applications in dynamic contact force tracking in uncertain environment, it is not an effective solution. In this paper, a new adaptive variable impedance control is proposed for force tracking which has the capability to track the dynamic desired force and compensate for uncertainties (in terms of unknown geometrical and mechanical properties) in environment. In this study, the contact force model of robot end-effector and the environment is analyzed. Specifically, the contact force is used as the feedback force of a position-based impedance controller to actively track the dynamic desired force in uncertain environment. To adapt any environment stiffness uncertainties, a modified impedance control is proposed. To reduce the force tracking error caused by environment location uncertainty, an adaptive variable impedance control is implemented for the first time by adjusting the impedance parameters on-line based on the tracking error to compensate the unknown environment and the dynamic desired force. Furthermore, stability and convergence of the adaptive variable impedance control are demonstrated for a stable force tracking execution. Simulations and experiments to compare the performance of force tracking with the constant impedance control and the adaptive variable impedance control, perspectively, are conducted. The results strongly prove that the proposed approach can achieve better force tracking performance than the constant impedance control.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jinjun Duan, Yahui Gan, Ming Chen, Xianzhong Dai,