Changchun Institute of Optics,Fine Mechanics and Physics,CAS
| Neural network-based variable stiffness impedance control for internal/external forces tracking of dual-arm manipulators under uncertainties | |
| Y. Zhou, Z. Li, Y. Li and M. Zhu | |
| 2023 | |
| 发表期刊 | Control Engineering Practice
 |
| ISSN | 09670661 |
| 卷号 | 141 |
| 摘要 | The desired interaction between manipulators, objects, and environments has resulted in the internal/external force control for dual-arm manipulators being in increasing demand. Consequently, this study focused on the internal/external force tracking for dual-arm manipulator systems under external disturbances, geometries, and stiffness uncertainties which continuously lead to unsatisfactory internal force tracking. The proposed scheme is based on a two-level adaptive impedance control scheme, where the stiffness coefficient is adjusted to adapt to uncalibrated objects. An object-level hybrid impedance controller was used to regulate the external disturbance to produce a compliant response. A manipulator-level neural network-based variable stiffness impedance controller (NNVSIC) was proposed to regulate the internal force under various uncertainties. Additionally, an adaptive wavelet neural network was designed to compensate for the geometric estimation errors of the object. The variable stiffness coefficient could automatically adapt to an unknown object during the cooperation process. One advantage of the proposed method is that no prior knowledge was required. The same controller parameters could be adapted to various objects. The asymptotic stability of the proposed NNVSIC was proven via Lyapunov stability analysis. A series of experiments were conducted using two self-developed nine-degrees-of-freedom redundant manipulators. Furthermore, hard and soft objects of various geometries and stiffnesses were used to verify the effectiveness of the algorithm. The experimental results demonstrated the efficiency and superiority of the proposed controller through performance comparison with various algorithms. © 2023 Elsevier Ltd |
| DOI | 10.1016/j.conengprac.2023.105714 |
| URL | 查看原文 |
| 收录类别 | ei |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ciomp.ac.cn/handle/181722/68274 |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | Y. Zhou, Z. Li, Y. Li and M. Zhu. Neural network-based variable stiffness impedance control for internal/external forces tracking of dual-arm manipulators under uncertainties[J]. Control Engineering Practice,2023,141. |
| APA | Y. Zhou, Z. Li, Y. Li and M. Zhu.(2023).Neural network-based variable stiffness impedance control for internal/external forces tracking of dual-arm manipulators under uncertainties.Control Engineering Practice,141. |
| MLA | Y. Zhou, Z. Li, Y. Li and M. Zhu."Neural network-based variable stiffness impedance control for internal/external forces tracking of dual-arm manipulators under uncertainties".Control Engineering Practice 141(2023). |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Neural network-based（9375KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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