Please use this identifier to cite or link to this item: https://doi.org/10.1109/AIM.2013.6584215
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dc.titlePID gain scheduling by parametric model predictive control
dc.contributor.authorNguyen, M.H.-T.
dc.contributor.authorTan, K.K.
dc.contributor.authorTeo, C.S.
dc.date.accessioned2014-06-19T03:23:50Z
dc.date.available2014-06-19T03:23:50Z
dc.date.issued2013
dc.identifier.citationNguyen, M.H.-T.,Tan, K.K.,Teo, C.S. (2013). PID gain scheduling by parametric model predictive control. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013 : 944-948. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/AIM.2013.6584215" target="_blank">https://doi.org/10.1109/AIM.2013.6584215</a>
dc.identifier.isbn9781467353199
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71445
dc.description.abstractThis paper considers the problem of augmenting the PID structure with the MPC functionality of constraint handling and optimization. Firstly, we review the MPC framework which can be built from a model and a linear feedback gain. This linear gain can be any preexisting multi-loop PID design in the unconstrained case, or based on the two stabilizing PI/PID design for multivariable systems we introduce here. The resulting controller is a feedforward PID mapping, a straightforward form without the need of tuning PID to fit an optimal input. Secondly, the parametric solution of MPC further suggests a PID network implementation by utilizing gain scheduling schemes available in industry. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/AIM.2013.6584215
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/AIM.2013.6584215
dc.description.sourcetitle2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013
dc.description.page944-948
dc.identifier.isiutNOT_IN_WOS
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