Bidirectional branch and bound for controlled variable selection. Part II: Exact local method for self-optimizing control

The selection of controlled variables (CVs) from available measurements through enumeration of all possible alternatives is computationally forbidding for large-dimensional problems. In Part I of this work [5], we proposed a bidirectional branch and bound (BAB) approach for subset selection problems and demonstrated its efficiency using the minimum singular value criterion. In this paper, the BAB approach is extended for CV selection using the exact local method for self-optimizing control. By redefining the loss expression, we show that the CV selection criterion for exact local method is bidirectionally monotonic. A number of novel determinant based criteria are proposed for fast pruning and branching purposes resulting in a computationally inexpensive BAB approach. We also establish a link between the problems of selecting a subset and combinations of measurements as CVs and present a partially bidirectional BAB method for selection of measurements, whose combinations can be used as CVs. Numerical tests using randomly generated matrices and binary distillation column case study demonstrate the computational efficiency of the proposed methods.