Model predictive control (MPC) has been an important and successful advanced control technology in various industries, mainly due to its ability to effectively handle complex systems with hard control constraints. At each sampling time, MPC solves a constrained optimal control problem online, based on the most recent state or output feedback to obtain a finite sequence of control actions, and only applies the first portion. MPC presents a very flexible optimal control framework that is capable of handling a wide range of industrial issues while incorporating state or output feedback to aid in the robustness of the design.
This book consists of a compilation of works covering a number of application domains, such as hydraulic fracturing, continuous pharmaceutical manufacturing, and mineral column flotation, in addition to works covering theoretical and practical developments in topics such as economic and distributed MPC. The purpose of this book is to assemble a collection of current research in MPC that handles practically-motivated theoretical issues as well as recent MPC applications, with the aim of highlighting the significant potential benefits of new MPC theory and design.