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Bae, Joonbum
Bio-Robotics and Control (BiRC) Lab
Research Interests
  • Design and control of physical human-robot interaction systems
  • Soft robotics
  • Intelligent interaction algorithms for virtual reality, tele-operation and rehabilitation
  • Bio-inspired robotics for improved mobility

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A Precise Neural-disturbance Learning Controller of Constrained Robotic Manipulators

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Title
A Precise Neural-disturbance Learning Controller of Constrained Robotic Manipulators
Author
Ba, Dang XuanBae, Joonbum
Issue Date
2021-03
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v.9, pp.50381 - 50390
Abstract
An adaptive robust controller is introduced for high-precision tracking control problems of robotic manipulators with output constraints. A nonlinear function is employed to transform the constrained control objective to new free variables that are then synthesized using a sliding-mode-like function as an indirect control mission. A robust nonlinear control signal is derived to ensure the boundedness of the main control objective without violation of physical output constraints. The control performance is improved by adopting a neural-network model with conditioned nonlinear learning laws to deal with nonlinear uncertainties and disturbances inside the system dynamics. A disturbance-observer-based control signal is additionally properly injected into the neural nonlinear system to eliminate the approximation error for achieving asymptotically tracking control accuracy. Performance of the overall control system is validated by intensive theoretical proofs and comparative simulation results.
URI
https://scholarworks.unist.ac.kr/handle/201301/50168
URL
https://ieeexplore.ieee.org/document/9388669
DOI
10.1109/ACCESS.2021.3069229
ISSN
2169-3536
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