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Process Systems Engineering
Distributed model predictive control based on dissipativity
Article first published online: 27 JUN 2012
DOI: 10.1002/aic.13868
Copyright © 2012 American Institute of Chemical Engineers (AIChE)
Additional Information
How to Cite
Tippett, M. J. and Bao, J. (2013), Distributed model predictive control based on dissipativity. AIChE J., 59: 787–804. doi: 10.1002/aic.13868
Publication History
- Issue published online: 20 FEB 2013
- Article first published online: 27 JUN 2012
- Accepted manuscript online: 11 JUN 2012 09:39AM EST
- Manuscript Revised: 6 JUN 2012
- Manuscript Received: 17 JAN 2012
Funded by
- Australian Research Council Discovery Project. Grant Number: DP1093045
- Australian Postgraduate Award scholarship
- Engineering Supplementary Award and UNSW Excellence Awards
- Abstract
- Article
- References
- Cited By
Keywords:
- process control;
- distributed model predictive control;
- dissipativity;
- dynamic supply rate;
- quadratic difference forms
A noncooperative approach to plant-wide distributed model predictive control based on dissipativity conditions is developed. The plant-wide process and distributed control system are represented as two interacting process and controller networks, with interaction effects captured by the dissipativity properties of subsystems and network topologies. The plant-wide stability and performance conditions are developed based on global dissipativity conditions, which in turn are translated into the dissipative trajectory conditions that each local model predictive control MPC must satisfy. This approach is enabled by the use of dynamic supply rates in quadratic difference forms, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2012 American Institute of Chemical Engineers AIChE J, 59: 787–804, 2013