This paper presents a two-step method for control-relevant model reduction of Volterra
series models. First, using the nonlinear IMC design as a basis, an explicit expression …

This short note describes how to extend a certain class of existing model reduction
techniques to take into account uncertainty in model parameters. The key idea of this …

This paper proposes an approach for obtaining reduced-order models of chemical
processes with application to the design of plantwide control systems. The approach is …

Selecting the order of an input–output model of a dynamical system is a key step toward the
goal of system identification. The false nearest neighbors algorithm (FNN) is a useful tool for …

Two formulations of a nonlinear model predictive control scheme based on the second-order
Volterra series model are presented. The first formulation determines the control action …

A new model-based optimizing controller for a set of nonlinear systems is proposed. The
nonlinear model set is based on a convex combination of two bounding linear models. An …

The problem of controlling a high-dimensional linear system subject to hard input and state
constraints using model predictive control is considered. Applying model predictive control …

The problem of controlling processes with a wide range of operating conditions is
addressed. The process dynamics is represented by the weighted‐sum of the linear model …

Abstract Recently, a linear Model Predictive Control (MPC) suitable for closed-loop re-
identification was proposed, which solves the potential conflict between the persistent …

A nonlinear controller synthesis scheme is presented that retains the original spirit and
characteristics of conventional (linear) model predictive control (MPC) while extending its …