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Physical and Data-Driven Optimization for Thin-Walled Manipulator Based on Motion Reliability
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AI
DOI：10.1109/TMECH.2025.3553522.png)
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In automated mechanical systems, there are strict requirements for the efficiency and accuracy of the movement of certain specific mechanisms, which are often achieved through feedback control. To determine how to select control parameters for a flexible thin-walled manipulator to achieve high-quality movement, we conducted optimization research based on motion accuracy reliability. We have constructed a coupled system dynamics model that integrates the mechanism and motor, allowing the introduction of a controller during the simulation process and considering the randomness of the load. Taking into account that the scale of solving flexible dynamics will result in huge computational costs for subsequent reliability and optimization analysis, we have introduced a data-driven numerical integration scheme. This scheme significantly reduces the time required for dynamics simulation while ensuring acceptable accuracy and adaptability. In the case study, the simulation time can be reduced by about 20 times through the support of data-driven technology while accurately capturing the motion patterns of the manipulator, including its flexible vibrations. Further optimization results indicate that we can improve motion efficiency by approximately $10 \%$ by designing control strategy parameters, while ensuring motion accuracy reliability and stability.
Keyword:
Deep neural network
electromechanical dynamics
motion accuracy reliability
thin-walled manipulator
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论文数:
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