Neural networks enhancedadaptive admittance control of optimized robot-environment interaction
文献类型：期刊论文
| 作者 | Chenguang Yang; Guangzhu Peng; Yanan Li; Rongxin Cui; Long Cheng ; Zhijun Li
|
| 刊名 | IEEE Transactions on Cybernetics
 |
| 出版日期 | 2018 |
| 关键词 | Admittance control neural networks (NNs) observer optimal adaptive control robot–environment interaction |
| DOI | 10.1109/TCYB.2018.2828654 |
| 英文摘要 | In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/23168]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 推荐引用方式 GB/T 7714 | Chenguang Yang,Guangzhu Peng,Yanan Li,et al. Neural networks enhancedadaptive admittance control of optimized robot-environment interaction[J]. IEEE Transactions on Cybernetics,2018. |
| APA | Chenguang Yang,Guangzhu Peng,Yanan Li,Rongxin Cui,Long Cheng,&Zhijun Li.(2018).Neural networks enhancedadaptive admittance control of optimized robot-environment interaction.IEEE Transactions on Cybernetics. |
| MLA | Chenguang Yang,et al."Neural networks enhancedadaptive admittance control of optimized robot-environment interaction".IEEE Transactions on Cybernetics (2018). |
入库方式： OAI收割
来源：自动化研究所
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