Model predictive control is a form of control in which the current control action is obtained by
solving, at each sampling instant, a finite horizon open-loop optimal control problem, using …

The paper provides a reasonably accessible and self-contained tutorial exposition on model
predictive control (MPC). It is aimed at readers with control expertise, particularly …

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 …

To enhance energy production from methane or resource recovery from digestate,
anaerobic digestion processes require advanced instrumentation and control tools. Over the …

This paper presents a novel method to address the actuator saturation for nonlinear hybrid
systems by directly incorporating user-defined input bounds in a controller design. In …

Modern agriculture is nowadays subject to regulations in terms of quality and environmental
impact and thus it is a field where the application of automatic control techniques has …

Compiling the most significant advances from nearly a decade of research, this reference
compares and evaluates a wide variety of techniques for the design, analysis, and …

Abstract Fuzzy model identification is an effective tool for the approx-imation of uncertain
nonlinear systems on the basis of measured data. The identification of a fuzzy model using …

Over the last few years, autonomous shipping has been under extensive investigation by the
scientific community where the main focus has been on ship maneuvering control and not …

This paper develops a general framework for the analysis and control of parabolic partial
differential equations (PDE) systems with input constraints. Initially, Galerkin's method is …