This paper formulates a state observer based nonlinear model predictive control (MFC)
technique using successive linearization. Based on local linear approximations of …

Based on presentation of the principles of the EKF and UKF for state estimation, we discuss
the differences of the two approaches. Four rather different simulation cases are considered …

Process control systems, such as distributed or scalable process control systems, can be
utilized in chemical, petro leum and other industrial processes. A typical process control …

This work deals with state estimation in the presence of delayed and infrequent
measurements. While most measurements (referred to as secondary measurements) are …

This paper provides a review of the available tuning guidelines for model predictive control,
from theoretical and practical perspectives. It covers both popular dynamic matrix control …

A new model predictive formulation to control MIMO nonlinear processes is presented. It
directly extends the model predictive concepts that have been exploited for the control of …

The nonlinear model predictive control (NMPC) is an on-line application based on nonlinear
convolution models. It is an appealing control methodology, but it is difficult to implement …

Two approaches to the on-line identification of parameters and states in systems described
by nonlinear ordinary differential equations are compared by using an example chemical …

Abstract Model predictive control (MPC) schemes such as MOCCA, DMC, MAC, MPHC, and
IMC use discrete step (or impulse) response data rather than a parametric model. They …

Chemical process plants typically have a large number of measured variables that contain
some degree of error. Accurate knowledge of the process states and parameters can be …