Linear model predictive control (LMPC) is well established as the industry standard for
controlling constrained multivariable processes. A major limitation of LMPC is that plant …

Nonlinear models that are composed of a linear dynamic element in series with a nonlinear
static element prove to be very attractive in describing the behaviour of many chemical …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

Gaussian Process (GP), as a probabilistic non-linear multi-variable regression model, has
been widely used in nonparametric Bayesian framework for the data based modelling of …

A fuzzy model predictive control (FMPC) approach is introduced to design a control system
for a highly nonlinear process. In this approach, a process system is described by a fuzzy …

This study presents the design of a fault detection and diagnosis (FDD) scheme, composed
from a bank of two types of observers, applied to linear parameter varying (LPV) systems …

In this paper a nonlinear model predictive control (NMPC) based on a Wiener model with a
piecewise linear gain is presented. This approach retains all the interested properties of the …

A robust fuzzy predictive control (RFPC) based on Takagi-Sugeno (TS) fuzzy model is
proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well …

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 …

A novel maximum likelihood solution to the problem of identifying parameters of a nonlinear
model under missing observations is presented. If the observations are missing, then it is …