An Iterative LMI Approach to Controller Design and Measurement Selection in Self-Optimizing Control

Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. The loss can further be reduced by controlling measurement combinations to constant values. Two methods for finding appropriate measurement combinations are the Null-space and the Exact local method. Both approaches offer sets with an infinite number of solutions that give the same loss. Since self-optimizing control is mainly concerned with minimizing the steady-state loss, little attention has been put on the dynamic performance when selecting measurement combinations