CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Neural learning enhanced teleoperation control of robots with uncertainties
Chenguang Yang; Junshen Chen; Long Cheng
2016
Conference Name 9th International Conference on Human System Interactions (HSI)
Conference DateJUL 06-08, 2016
Conference PlacePortsmouth
CountryEngland
AbstractFor most teleoperation tasks, it is desired that the telerobot manipulator follows timely and precisely the reference motion set at the master side. However, the conventional control approach may not guarantee the desired performance when there are dynamic uncertainties, especially when there is a notable variation of the telerobot's payload. In this paper, a neural learning based compensation mechanism has been exploited to overcome the effect of the unknown payload as well as uncertainties associated with the telerobot model and the environment. Guaranteed transient performance has been theoretically established. The deterministic learning technique has been employed, such that the neural learned knowledge can be efficiently reused. We performed comparative experiments and demonstrate the effectiveness of the proposed design techniques.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23135
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Chenguang Yang,Junshen Chen,Long Cheng. Neural learning enhanced teleoperation control of robots with uncertainties[C],2016.
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