It is desired for modern process systems to achieve optimal operation. However, operation at a pre-designed nominally optimal point may not necessarily be actually optimal due to realtime disturbances, measurement and control errors and uncertainties. Self-optimizing control aims to tackle this problem through feedback control. By carefully selecting or synthesizing controlled variables, a self-optimizing control system can
achieve optimal or near optimal operation in spite of the presence of disturbances, measurement and control errors and uncertainties.
Selection of controlled variables is a NP-hard problem due to its combinatorial nature althrough theoretical criteria have been developed for self-optimizing control. This package includes two solvers using the local worst case loss criterion. Both solvers are based on the bidirectional branch and bound approach developed recently by the author and Dr. Kariwala. The principles, efficiency and applications of these solvers are available in the recent paper to be published in Computers and Chemical Engineering, and available online at http://dx.doi.org/10.1016/j.compchemeng.2009.01.014
For a preprint copy of the paper, please contact the author.
Yi Cao (2022). Bidirectional Branch and Bound Solvers for Worst Case Loss Minimization (https://www.mathworks.com/matlabcentral/fileexchange/22632-bidirectional-branch-and-bound-solvers-for-worst-case-loss-minimization), MATLAB Central File Exchange. Retrieved .
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