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 identificat...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.展开更多
A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled （NUS） data. Due to the nonuniform sampling feature, the time interval from one recursio...A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled （NUS） data. Due to the nonuniform sampling feature, the time interval from one recursion step to the next varies and the parameter is always updated partially at each step. Furthermore, this identification method is applied to form a combined data compression method in NUS processes. The data to be compressed are first classified with respect to a series of potentially existing （possibly time-varying） models, and then modeled by the NUS identification method. The model parameters are stored instead of the identification output data, which makes the first compression. Subsequently, as the second step, the conventional swinging door trending method is carried out on the data from the first step. Numeric results from simulation as well as practical data are given, showing the effectiveness of the proposed identification method and fold increase of compression ratio achieved by the combined data compression method.展开更多
基金Supported by the China Postdoctoral Science Foundation Funded Project (No. 20080440386)
文摘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.
文摘A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled （NUS） data. Due to the nonuniform sampling feature, the time interval from one recursion step to the next varies and the parameter is always updated partially at each step. Furthermore, this identification method is applied to form a combined data compression method in NUS processes. The data to be compressed are first classified with respect to a series of potentially existing （possibly time-varying） models, and then modeled by the NUS identification method. The model parameters are stored instead of the identification output data, which makes the first compression. Subsequently, as the second step, the conventional swinging door trending method is carried out on the data from the first step. Numeric results from simulation as well as practical data are given, showing the effectiveness of the proposed identification method and fold increase of compression ratio achieved by the combined data compression method.
