DESIGN OF IDENTIFICATION EXPERIMENTS FOR ROBUST-CONTROL - A GEOMETRIC APPROACH FOR BIVARIATE PROCESSES

A new approach to the design of experiments is proposed to identify linear multiple-input-multiple-output (MIMO) models that will provide robust control. The experimental designs for identification are based on minimizing uncertainties in the structure of the multivariate model rather than simply the magnitude of identification error. The experimental designs are derived for steady-state robust stability of 2 x 2 systems using a geometric approach. This approach leads to a simple and unified design approach based on the singular value decomposition (SVD) of the process gain matrix. These robust or control-relevant identification designs for MIMO systems differ considerably from traditional designs developed for single-output systems. Typically in these new multivariate designs, the inputs are correlated, they are not binary sequences, and the magnitudes of the perturbations in low-gain directions are much larger than those in high-gain directions. The results are extended to identification under closed-loop conditions. Dual composition control of distillation processes is used to illustrate the physical interpretations and the effectiveness of the SVD-based design.