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dc.contributor.authorBlanchard, Haley
dc.contributor.authorAdegbege, Ambrose A.
dc.date.accessioned2016-10-26T21:03:26Z
dc.date.available2016-10-26T21:03:26Z
dc.date.issued2016
dc.descriptionDepartment of Electrical and Computer Engineeringen_US
dc.description.abstractThis project proposes an iterative first-order gradient method for solving convex quadratic programming problems, which involve the optimization of a quadratic function with multiple variables subject to linear constraints. It has prospects for use in model predictive control (MPC). The proposed successive overrelaxation (SOR)-like method utilizes a matrix-splitting scheme and can handle problems involving both state and input constraints. The corresponding algorithm can easily be implemented and can be tuned for optimum performance and global convergence. The method’s performance is analyzed and compared with those of other well-known and state-of-the-art methods via MATLAB simulations.en_US
dc.description.sponsorshipMUSE (Mentored Undergraduate Summer Experience)en_US
dc.description.sponsorshipCollege of New Jersey (Ewing, N.J.). Office of Academic Affairsen_US
dc.language.isoen_USen_US
dc.rightsFile access restricted due to FERPA regulations
dc.titleAn SOR-like method for fast model predictive controlen_US
dc.typePosteren_US
dc.typePresentationen_US
dc.typeTexten_US
dc.identifier.handlehttps://dr.tcnj.edu/handle/2900/660


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