Optimized Admittance Control for Manipulators Interacting with Unknown Environment

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can...

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Bibliographic Details
Main Authors: Kong, Haiyi, Peng, Guangzhu, Li, Guang, Yang, Chenguang
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Adaptive admittance control
Adaptive dynamic programming
Control design
Dynamic programming
End effectors
Environment position
Heuristic algorithms
Iterative methods
Optimized admittance adaptation
Trajectory
Unknown environment
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Summary:This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.
ISSN:2643-2978
DOI:10.1109/ICIT58233.2024.10540834