Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

Chenguang Yang, Guangzhu Peng, Yanan Li, Rongxin Cui, Long Cheng, Zhijun Li

Research output: Contribution to journalArticlepeer-review

174 Scopus citations

Abstract

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.

Original languageEnglish
Article number8356104
Pages (from-to)2568-2579
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume49
Issue number7
DOIs
StatePublished - Jul 2019

Keywords

  • Admittance control
  • neural networks (NNs)
  • observer
  • optimal adaptive control
  • robot-environment interaction

Fingerprint

Dive into the research topics of 'Neural networks enhanced adaptive admittance control of optimized robot-environment interaction'. Together they form a unique fingerprint.

Cite this