Reference trajectory generation for force tracking impedance control by using neural network-based environment estimation

Heng Wang, K. H. Low, Michael Yu Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

19 Citations (Scopus)

Abstract

This paper presents a reference trajectory generation approach for impedance control by using neural networks to estimate the environment dynamics. In this method, the environment dynamics is estimated by a neural network (NN1), which constructs the relationship between the environment deformation and its first and second derivatives, and the interaction force. Another network (NN2) is then used to approximate the statics of the environment, which is the relationship between the interaction force and the deformation. The major advantage of the proposed method is that no exact environment model is required, so that it suites for operations on any unstructured environments. Furthermore, the neural networks have the capability of learning, due to which the precision of the generated reference trajectory will continuously be increased as the robot-environment interaction lasts. The system performance by using the proposed method is evaluated by simulations.

Original languageEnglish
Title of host publication2006 IEEE Conference on Robotics, Automation and Mechatronics
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)1424400244, 9781424400249
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventIEEE Conference on Robotics, Automation and Mechatronics 2006 - Bangkok, Thailand
Duration: 7 Jun 20069 Jun 2006

Conference

ConferenceIEEE Conference on Robotics, Automation and Mechatronics 2006
Country/TerritoryThailand
CityBangkok
Period7/06/069/06/06

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