Abstract:
Most of the available methods for selection of input-output pairings for
decentralized control require evaluation of all alternatives to find the optimal
pairings. As the number of alternatives grows rapidly with process dimensions,
pairing selection through an exhaustive search can be computationally forbidding
for large-scale processes. Furthermore, the different criteria can be
conflicting necessitating pairing selection in a multiobjective optimization
framework. In this paper, an efficient branch and bound (BAB) method for
multiobjective pairing selection is proposed. The proposed BAB method is
illustrated through a biobjective pairing problem using selection criteria
involving the relative gain array and the mu-interaction measure. The
computational efficiency of the proposed method is demonstrated by using
randomly generated matrices and the large-scale case study of cross-direction
control. (C) 2010 Elsevier Ltd. All rights reserved.
