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The joint parameter and time-delay estimation problems for a class of nonlinear multirate time-delay system with uncertain output delays are addressed in this paper. The practical process typically has time-delay properties and the process data are often multirate, sampled with output data inevitably corrupted by uncertain delays. The linear parameter varying (LPV) finite impulse response (FIR) multirate time-delay model is initially built to describe the considered system. The problems of over-parameterization and the existence of both continuous model parameters and discrete time-delays have made the conventional maximum likelihood difficult to solve the considered problems. In order to handle these problems, the joint parameter and time-delay estimation for the LPV FIR multirate time-delay model are formulated in the expectation-maximization scheme, and the algorithm to estimate the model parameters and time-delays is derived, simultaneously based on multirate process data. The efficacy of the proposed method is verified through a numerical simulation and a practical chemical plant. [ABSTRACT FROM AUTHOR]

Apart from strong nonlinearity and time-varying behaviors in industrial processes, the hidden time-delay information, which is unfortunately overlooked in most existing modeling methods, should also be taken into account in soft sensor modeling. In view of this, a novel soft sensor, referred to as local time-delay reconstruction based moving window time difference Gaussian process regression (LTR-MWTDGPR), is proposed in this paper. To deal with the time-delay, a fuzzy curve analysis based local time-delay parameter extraction procedure is performed along with a strategy of a moving window, which simultaneously captures the process time-varying feature. Then the local window training dataset and new query sample are reconstructed according to the time-delay parameter set at the next sampling instant. Afterwards, the time difference Gaussian process regression is employed to handle the drifting feature of local reconstructed dataset. The effectiveness and accuracy of the proposed LTR-MWTDGPR approach in predicting quality variables are verified through a real sulfur recovery unit and an industrial debutanizer column. [ABSTRACT FROM AUTHOR]

This paper is concerned with theidentification problems of thelinear parameter varying (LPV) system with missing output in the presenceof the time-delay. A multiple-model approach is adopted. Local modelsvarying from one operating point to another are first described byfinite impulse response (FIR) models. To handle missing output andtime-delay, the expectation-maximization (EM) algorithm is utilizedto estimate the unknown parameters and the time-delay simultaneously.Output Error (OE) models are widely used in controller design. Therefore,the auxiliary model principle is employed to recover the OE modelsbased on the initially identified FIR models. The EM algorithm isthen used again to refine the unknown parameters of the OE modelswith the complete data set to obtain the final global model. Simulationexamples are presented to demonstrate the performance of the proposedmethod. [ABSTRACT FROM AUTHOR]