Direct filter tuning and optimization in multivariable identification

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. A previous contribution approaches the first using matchable-observable models originally developed in the adaptive control literature. This paper uses direct or derivative-free optimization to design filters. The accuracy, robustness and moderate computational demands of the methods is demonstrated via simulations with randomly generated models. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature.