| 引用本文: | 孙浪浪,何邵颖,徐云雯,陈友仁,潘旭华.数据驱动下的多指混杂机械臂控制与抓取[J].控制理论与应用,2025,42(12):2477~2486.[点击复制] |
| SUN Lang-lang,HE Shao-ying,XU Yun-wen,CHEN You-ren,PAN Xu-hua.Data-driven control and grasping of multi-finger hybrid robotic arm[J].Control Theory & Applications,2025,42(12):2477~2486.[点击复制] |
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| 数据驱动下的多指混杂机械臂控制与抓取 |
| Data-driven control and grasping of multi-finger hybrid robotic arm |
| 摘要点击 112 全文点击 38 投稿时间：2024-06-14 修订日期：2025-10-26 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40325 |
| 2025,42(12):2477-2486 |
| 中文关键词 混杂机械臂 模型预测控制 输入映射 数据驱动 抓取控制 |
| 英文关键词 hybrid robotic arm model predictive control input mapping data-driven grasping control |
| 基金项目 国家自然科学基金项目(61973214,62261160385,62103271)资助. |
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| 中文摘要 |
| 本文设计了多指混杂机械臂控制系统及其抓取控制方法,对于抓取任务,刚柔混杂的设计结构同时提高了
控制的精确性以及与物体交互的安全性.本文分别采用旋量理论和分段常曲率方法对机械臂的刚体和柔性部分进
行运动学建模,最终得到基于雅克比矩阵的刚柔混杂机械臂一体化模型.此外,为削弱模型偏差对系统控制性能的
影响,本文提出了基于输入映射的数据驱动和模型预测控制相结合的方法,用历史数据替换部分不精确的系统模
型, 同时利用历史数据的线性组合来构造当前时刻的状态和控制输入,以完成对柔性夹爪位姿的控制,柔性夹爪末
端的轨迹跟踪仿真反映该控制器的性能良好.在此基础上,本文设计了基于被抓物体姿态的精确抓取与包络抓取方
法, 并通过ArUco方块抓取实验证实了该方法的有效性. |
| 英文摘要 |
| In this paper, a multi-finger hybrid robotic arm control system and its grasping control method are designed.
The rigid-flexible hybrid structure enhances both the accuracy of control and the safety of object interaction during grasping
tasks. Kinematic modeling of the rigid and flexible components of the robotic arm is performed using screw theory and the
piecewise constant curvature method, respectively. An integrated model of the rigid-flexible hybrid robotic arm, based on
the Jacobian matrix, is then derived. To mitigate the impact of model inaccuracies on system performance, a novel approach
is proposed that combines data-driven techniques with model predictive control. This method replaces portions of the
imprecise system model with historical data and constructs the current state and control inputs through a linear combination
of this data. The effectiveness of this approach in controlling the posture of the flexible gripper is demonstrated through
favorable trajectory tracking results in simulations. Building upon this, an accurate grasping and enveloping grasping
method based on the posture of the grasped object is developed, and the validity of the method is confirmed through ArUco
cube grasping experiments. |
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