报告详情
Physics-Informed Neural Network-Based Adaptive Model Predictive Control for Visual Servoing of Robot Manipulators
编号：88
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更新：2025-05-06 15:09:34 浏览：6次
口头报告
报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
In image-based visual servoing applications, the depth information of the feature points is inherently time-varying, which can lead to intractable computational challenges when conventional nonlinear model predictive control (NMPC) approaches are implemented. Furthermore, the depth information generally remains unavailable in vision-based measurements obtained through two-dimensional imaging systems. To address these challenges, a physics-informed neural network (PINN) is initially designed to compensate for these uncertain parameters minimizing the discrepancy between the linear system and the actual model. And that, by leveraging a more accurate model obtained through successive linearization, the adaptive model predictive control based on PINN (PINN-based AMPC) method is developed. The proposed control framework offers the advantage of generating relatively accurate system models with limited training data, while achieving rapid location of static object. Finally, the effectiveness of the proposed control method is demonstrated through a series of simulations conducted on a Universal Robots 3 (UR3) manipulator.
关键词
image-based visual servoing,physics-informed neural networks,depth estimation,model predictive control,robot manipulator
报告人
Jiaqi Tang
Student Shanghai University of Electric Power稿件作者
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重要日期
-
会议日期
06月05日
2025
至06月08日
2025
-
04月30日 2025
初稿截稿日期
主办单位
IEEE PELS
IEEE
IEEE
承办单位
Southeast University
联系方式
- Quanwei Wu
- pr******@pub.seu.edu.cn
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