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Model Predictive Control of Running Biped Robot

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dc.contributor.authorCho, Jaeuk-
dc.contributor.authorPark, Jong Hyeon-
dc.date.accessioned2022-12-20T05:12:46Z-
dc.date.available2022-12-20T05:12:46Z-
dc.date.created2022-12-07-
dc.date.issued2022-11-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172894-
dc.description.abstractWith the feet of a biped robot attached insecurely to a terrain, its stability is strongly affected by the characteristics of the terrain on which it runs. Therefore, for stable bipedal running, online motion control based on the states of the robot and the environment is needed. This paper proposes a method for online motion control of a running biped robot on an uneven terrain based on a dual linear inverted pendulum model (D-LIPM) and hierarchical control which consists of linear model predictive control (MPC) and quadratic-problem (QP) based momentum control. The D-LIPM, which splits the nonlinear dynamics model of the running biped robot into two linear models under some assumptions, is proposed to generate the running motion through linear MPC. The D-LIPM is applied to the proposed hierarchical control for stable bipedal running. In the first stage of hierarchy, linear MPC is employed to generate the trajectory of the center of mass (COM) based on the dynamics of D-LIPM to overcome terrain uncertainties such as elevation levels and surface conditions at foot-landing sites. In the second stage, momentum control based on a QP solver is used to generate the angular motions of the robot while following the COM trajectory. Computer simulations with uncertainties on the running terrain were carried out to measure the performance of the proposed method.-
dc.language영어-
dc.language.isoen-
dc.publisherMDPI-
dc.titleModel Predictive Control of Running Biped Robot-
dc.typeArticle-
dc.contributor.affiliatedAuthorPark, Jong Hyeon-
dc.identifier.doi10.3390/app122111183-
dc.identifier.scopusid2-s2.0-85141653241-
dc.identifier.wosid000883437000001-
dc.identifier.bibliographicCitationAPPLIED SCIENCES-BASEL, v.12, no.21, pp.1 - 20-
dc.relation.isPartOfAPPLIED SCIENCES-BASEL-
dc.citation.titleAPPLIED SCIENCES-BASEL-
dc.citation.volume12-
dc.citation.number21-
dc.citation.startPage1-
dc.citation.endPage20-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusWALKING PATTERN GENERATION-
dc.subject.keywordPlusPREVIEW CONTROL-
dc.subject.keywordAuthorbiped robot-
dc.subject.keywordAuthorrunning-
dc.subject.keywordAuthormodel predictive control (MPC)-
dc.subject.keywordAuthorvelocity change-
dc.subject.keywordAuthorfoot placement-
dc.subject.keywordAuthoruneven terrain-
dc.identifier.urlhttps://www.mdpi.com/2076-3417/12/21/11183-
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