Matchable-observable linear models for multivariable identification: Structure selection and experimental results

Identification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. In previous contributions we approached the first using matchable-observable models originally developed in the adaptive control literature, and used direct or derivative-free optimization to design filters. In this paper we show a simple and effective structure-selection method and demonstrate its accuracy, robustness and moderate computational demands using data from an industrial evaporator and experimental results with a twin rotor.