To
reduce the effort and cost of model maintenance in model predictive
control (MPC) systems, this paper explored a model deficiency diagnosis
and improvement method by the assessment of model residual and optimization
of disturbance model. A model quality index (MQI) method was first
presented to evaluate the model performance with the routine input
and output process data. Based on MQI, a leave-one-out method was
proposed to further assess the performances of submodels in multi-input–multi-output
(MIMO) MPC processes. The root deficient submodel could be diagnosed
based on a comparison of the overall MQI with the submodel index by
moving it to the disturbance channel. Further, a model performance
improvement method through the upgraded optimal disturbance model
was proposed to enhance the model performance without altering the
control model. The experiment results on the Wood–Berry distillation
column process and an industrial process indicated the validity of
the proposed model diagnosis and improvement method.
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.