Optimal Fuzzy Impedance Control for a Robot Gripper Using Gradient Descent Iterative Learning Control in Fuzzy Rule Base Design
Source abstract
Abstract
This paper proposes a novel control approach for a robot gripper in which the impedance control, fuzzy logic control, and iterative learning control are combined in the same control schema. The impedance control is used to keep the gripping force at the desired value. The fuzzy impedance controller is designed to estimate the best impedance parameters in real time when gripping unknown objects. The iterative learning control process is employed to optimize the sample dataset for designing the rule base to enhance the effectiveness of the fuzzy impedance controller. Besides, the real-time gripping force estimator is designed to keep an unknown object from sliding down when picking it up. The simulation and experiment are implemented to verify the proposed method. The comparison with another control method is also made by repeating the experiments under equivalent conditions. The results show the feasibility and superiority of the proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Iterative Learning Control Systems · Advanced machining processes and optimization · Soft Robotics and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Faculty of Engineering and Technology, Kien Giang University, Kien Giang 920000, Vietnam
Center of Automation and Control, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Funding
Ministry of Science and Technology, Taiwan
References (28)
Show source-provided references
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