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-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.