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Bidirectional branch and bound for controlled variable selection. Part III: local average loss minimization

By Vinay Kariwala and Yi Cao


The selection of controlled variables (CVs) from available measurements through exhaustive search is computationally forbidding for large-scale processes. We have recently proposed novel bidirectional branch and bound (B-3) approaches for CV selection using the minimum singular value (MSV) rule and the local worst- case loss criterion in the framework of self-optimizing control. However, the MSV rule is approximate and worst-case scenario may not occur frequently in practice. Thus, CV selection by minimizing local average loss can be deemed as most reliable. In this work, the B-3 approach is extended to CV selection based on local average loss metric. Lower bounds on local average loss and, fast pruning and branching algorithms are derived for the efficient B-3 algorithm. Random matrices and binary distillation column case study are used to demonstrate the computational efficiency of the proposed method

Topics: Branch and bound combinatorial optimization control structure design controlled variables self-optimizing control self-optimizing control algorithm combination
Publisher: IEEE
Year: 2010
DOI identifier: 10.1109/TII.2009.2037494
OAI identifier:
Provided by: Cranfield CERES

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