Process modeling and optimizing control based on sparse nonuniformly sampled data

In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.

In this paper,a process modeling and related optimizing control for nonuniformly sampled ( NUS)systems are addressed.By using a proposed nonuniform integration filter and subspace method estimation,an identification method of NUS systems is developed,based on which either an output soft sensor or ahidden state estimator is developed.The optimizing control is implemented by replacing the sparsely-measured/immeasurable variable with the estimated one.Examples of optimizing control problem are given.The proposed optimizing control strategy in the simulation examples is verified to be very effective.