Nonlinear predictive control of irregularly sampled multirate systems using blackbox observers

A new identification method for dual-rate Hammerstein systems**This work was supported by the National Natural Science Foundation of China (No. 61403165) and supported by the Natural Science Foundation of Jiangsu Province (No. BK20131109)

“…The identification methods for dual-rate systems can be roughly divided into two categories: the lifting technique [19][20][21][22] (2) the number of the resulting unknown parameters of the identification model is huge. The missing output identification (MOI) model was first presented in [26].…”

“…The identification methods for dual-rate systems can be roughly divided into two categories: the lifting technique [19][20][21][22] and the polynomial transformation technique [23-⋆ This work was supported by the National Natural Science Foundation of China (No. 61403165) and supported by the Natural Science Foundation of Jiangsu Province (No.…”

A Moving Window Formulation for Recursive Bayesian State Estimation of Systems with Irregularly Sampled and Variable Delays in Measurements

“…The proposed moving window state estimator was implemented on the data collected from the benchmark heater–mixer experimental setup available at the Automation Laboratory, Department of Chemical Engineering, Indian Institute of Technology Bombay.…”

Modelling and identification for non-uniformly periodically sampled-data systems

“…It is well known that the AM-GESG algorithm in (16) to (18) has a slow convergence rate (see the example later) [24,25]. To speed up the convergence rate and improve the estimation accuracy, we expand the scalar innovation to an innovation vector by applying the multi-innovation identification theory [19] and derive an AM-MI-GESG algorithm.…”

“…Research activities on non-uniformly sampled-data systems exist: Larsson et al [14,15] presented an identification algorithm for continuous-time AR models and ARX models by replacing the derivative operator with an approximation and using the available non-uniformly discrete-time data; Sanchis and Albertos [16] applied pseudo-linear recursive algorithms to estimate the parameters of systems with irregularly sampled output; Zhu et al [17] used an output error method to identify the fast updated, slowly and non-uniformly sampled-data systems; Liu et al [11] proposed an auxiliary model based recursive least squares identification algorithm to estimate the parameters of nonuniformly sampled-data systems; Ding et al [12] discussed the reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems. Furthermore, Sheng et al [6] derived lifted state-space models for nonuniformly updated and sampled systems and proposed a new generalised predictive control algorithm, taking into account the causality constraint; Srinivasarao et al [18] developed an inferential non-linear model predictive control scheme based on a non-linear fast rate model that is identified from irregularly sampled multirate data.…”