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dc.contributor.authorCao, Gen_NZ
dc.contributor.authorLai, Een_NZ
dc.contributor.authorAlam, Fen_NZ
dc.date.accessioned2017-07-23T23:18:59Z
dc.date.available2017-07-23T23:18:59Z
dc.date.copyright2017en_NZ
dc.identifier.citationIn Control, Automation and Robotics (ICCAR), 2016 2nd International Conference on (pp. 200-206). IEEE.
dc.identifier.issn0921-0296en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/10667
dc.description.abstractThe Model Predictive Control (MPC) trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. In this article, the dynamic models of the quadrotor are obtained purely from operational data in the form of probabilistic Gaussian Process (GP) models. This is different from conventional models obtained through Newtonian analysis. A hierarchical control scheme is used to handle the trajectory tracking problem with the translational subsystem in the outer loop and the rotational subsystem in the inner loop. Constrained GP based MPC are formulated separately for both subsystems. The resulting MPC problems are typically nonlinear and non-convex. We derived a GP based local dynamical model that allows these optimization problems to be relaxed to convex ones which can be efficiently solved with a simple active-set algorithm. The performance of the proposed approach is compared with an existing unconstrained Nonlinear Model Predictive Control (NMPC) algorithm and an existing constrained nonlinear GP based MPC algorithm. In the first comparison, simulation results show that the two approaches exhibit similar trajectory tracking performance. However, our approach has the advantage of incorporating constraints on the control inputs. In the second comparison, simulation results demonstrate that our approach only requires 20% of the computational time for the existing nonlinear GP based MPC.
dc.publisherSpringer Verlagen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs10846-017-0549-y
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.subjectQuadrotor trajectory tracking; Model predictive control; Gaussian process
dc.titleGaussian Process Model Predictive Control of an Unmanned Quadrotoren_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1007/s10846-017-0549-yen_NZ
pubs.elements-id278305
aut.relation.journalJournal of Intelligent and Robotic Systemsen_NZ


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