Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/56781
Title: Neural network adaptive impedance control of constrained robots
Authors: Huang, L.
Ge, S.S. 
Lee, T.H. 
Keywords: Adaptive control
Constrained robots
Force control
Impedance control
Neural network
Issue Date: 2004
Source: Huang, L.,Ge, S.S.,Lee, T.H. (2004). Neural network adaptive impedance control of constrained robots. International Journal of Robotics and Automation 19 (3) : 117-124. ScholarBank@NUS Repository.
Abstract: Traditional impedance control requires exact dynamic modelling of robots and constraint environments. Although some robust or adaptive control methods are used to handle uncertainties in constrained robot systems, very few of them consider uncertainties of both the constraint and the robot. The desired impedance is also fixed regardless of the types of the constraints. In this article, a model-free neural-network-based adaptive impedance control scheme is developed considering uncertainities of both the robot and the constraint. The neural network and the desired impedance are tuned directly with the impedance tracking and the position tracking errors respectively. The controller guarantees that the desired impedance is achieved asymptotically with good position/force-tracking performances. Simulation results are provided to verify the effectiveness of the scheme.
Source Title: International Journal of Robotics and Automation
URI: http://scholarbank.nus.edu.sg/handle/10635/56781
ISSN: 08268185
Appears in Collections:Staff Publications

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