Abstract Model-based control strategies are widely used for optimal operation of chemical
processes to respond to the increasing performance demands in the chemical industry. Yet …

This is a revised version of the 1984 book of the same name but considerably modified and
enlarged to accommodate the developments in recursive estimation and time series …

Compared with the systems with white noise disturbances, the parameter identification of the
systems with colored noises (ie, correlated noises) is more difficult. In this letter, we use the …

This paper discusses the importance and relevance of direct continuous-time system
identification and how this relates to the solution for model identification problems in …

For many years, various methods for the identification and estimation of parameters in linear,
discrete-time transfer functions have been available and implemented in widely available …

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear
Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control …

A common problem in the context of linear parameter-varying (LPV) systems is how input-
output (IO) models can be efficiently realized in terms of state-space (SS) representations …

This paper is concerned with identification of linear parameter varying (LPV) systems in an
input–output setting with Box–Jenkins (BJ) model structure. Classical linear time invariant …

In many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV)
plants by linear parameter-varying (LPV) models and design controllers based on such …

The direct identification and estimation of continuous-time models from sampled data is now
mature. This paper does not present any new methodology, nor does it compare the …