A multiple model approach for predictive control of nonlinear hybrid systems
DSpace at IIT Bombay
View Archive InfoField | Value | |
Title |
A multiple model approach for predictive control of nonlinear hybrid systems
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Creator |
NANDOLA, NARESH N
BHARTIYA, SHARAD |
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Subject |
nonlinear systems
bayesian networks computational efficiency nonlinear programming |
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Description |
This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.
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Publisher |
Elsevier
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Date |
2009-02-18T09:13:30Z
2011-11-25T17:14:57Z 2011-12-26T13:06:10Z 2011-12-27T05:53:59Z 2009-02-18T09:13:30Z 2011-11-25T17:14:57Z 2011-12-26T13:06:10Z 2011-12-27T05:53:59Z 2008 |
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Type |
Article
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Identifier |
Journal of Process Control 18(2), 131-148
0959-1524 http://dx.doi.org/10.1016/j.jprocont.2007.07.003 http://hdl.handle.net/10054/706 http://dspace.library.iitb.ac.in/xmlui/handle/10054/706 |
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Language |
en
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