Optimal
input design is the process of generating informative inputs
that can be used to generate good quality dynamic models using minimal
resources. In this work, we propose an optimization-based input design
method for identification of ill-conditioned and interactive MIMO
systems. The interactions of the inputs, outputs, and ill-conditionality
of system are accounted in the formulation. The resulting problem
is convex where the decision variables are the input autocorrelation
and cross-correlation coefficients. The inputs are realized as white
noise filtered through an m-tap FIR filter. The filter coefficients
are obtained by the spectral factorization. These proposed ideas are
illustrated using simulation of two well-studied problems: (i) heat
exchanger system; (ii) high purity distillation column. The system
response obtained using conventional D-optimal inputs shows alignment
in one particular direction while the system outputs in the proposed
formulation show good distribution in the output space. The performance
of the inputs signal is also compared based on scattering factor,
crest factor, and fit percent of the identified model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.