Model predictive control (MPC) has been an important and successful advanced control technology in process industries, mainly due to its ability to handle effectively 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 robustness of the design.

Traditionally, centralized MPC with quadratic cost functions had dominated the focus of MPC research. Advances in computing, communication and sensing technologies in the last decades have enabled us to look beyond the traditional MPC and brought new challenges and opportunities in MPC research. Two important examples of this technology-driven development are distributed MPC (in which multiple local MPC controllers carry out their calculations in separate processors collaboratively) and economic MPC (in which a general economic cost function that typically is not quadratic is optimized). There are already many results on distributed MPC and economic MPC. However, there are still many important problems that need investigation within and beyond distributed and economic MPC. Along with the theoretical development in MPC, we are also witnessing the application of MPC to many non-traditional control or scheduling problems. Some examples are the use of MPC in the treatment of diabetes, management of hemoglobin in anemia, irrigation scheduling in agriculture, and coordination of distributed energy generation systems.

The purpose of this Special Issue is to assemble a collection of current research in MPC that handles practically-motivated theoretical issues, as well as recent MPC applications to highlight the significant potential benefits of new MPC theory and design.