Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

The paper provides a review of the different approaches of Model Predictive Control (MPC)
to deal with the nonlinearities and transient behavior associated with pH and its control …

This paper presents a new approach to the identification of pH processes based on the
Wiener model structure (a dynamic linear element in series with a static nonlinearity). A …

This paper is concerned with the identification of a nonlinear process which operates over
several working points with consideration of transition dynamics between the working points …

In recent years, new paradigms have emerged to replace-or augment-the traditional,
mathematically based approaches to optimization. The most powerful of these are genetic …

pH control is recognized as an industrially important, yet notoriously difficult control problem.
Wiener models, consisting of a linear dynamic element followed in series by a static …

This paper is a summary of the research development in the rational (total) nonlinear
dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined …

A method of nonlinear model predictive control based on an identified LPV model is
proposed. In process identification, a linear parameter varying (LPV) model approach is …

In this paper, we propose a robust multiple-model linear parameter varying (LPV) approach
to identification of the nonlinear process contaminated with outliers. The identification …

The performance of model predictive controllers (MPCs) strongly depends on the precision
of the prediction model. Nonlinear systems, such as neutralization reactors, provide special …