Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 50390 | - |
dc.citation.startPage | 50381 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.contributor.author | Ba, Dang Xuan | - |
dc.contributor.author | Bae, Joonbum | - |
dc.date.accessioned | 2023-12-21T16:11:10Z | - |
dc.date.available | 2023-12-21T16:11:10Z | - |
dc.date.created | 2021-03-16 | - |
dc.date.issued | 2021-03 | - |
dc.description.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. | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.50381 - 50390 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3069229 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85103753878 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/50168 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9388669 | - |
dc.identifier.wosid | 000638391200001 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A Precise Neural-disturbance Learning Controller of Constrained Robotic Manipulators | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Engineering; Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Robots | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Uncertainty | - |
dc.subject.keywordAuthor | Nonlinear dynamical systems | - |
dc.subject.keywordAuthor | Manipulator dynamics | - |
dc.subject.keywordAuthor | Disturbance observers | - |
dc.subject.keywordAuthor | System dynamics | - |
dc.subject.keywordAuthor | Robotics | - |
dc.subject.keywordAuthor | manipulators | - |
dc.subject.keywordAuthor | adaptive robust control | - |
dc.subject.keywordAuthor | nonlinear control | - |
dc.subject.keywordAuthor | position control | - |
dc.subject.keywordAuthor | output constrained control | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | disturbance observer | - |
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