Special Issue "New Directions on Model Predictive Control"
A special issue of Mathematics (ISSN 2227-7390).
Deadline for manuscript submissions: closed (31 July 2018)
A printed edition of this Special Issue is available here.
Prof. Dr. Jinfeng Liu
Department of Chemical & Materials Engineering, 13-269 Donadeo Innovation Center for Engineering, University of Alberta, 9211-116 Street, Edmonton, AB, T6G 1H9, Canada
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Interests: networked process control systems; distributed predictive control of nonlinear systems; distributed state estimation; networked plant-wide monitoring and fault-tolerant control; optimal operation of energy systems
Prof. Dr. Helen E. Durand
Department of Chemical Engineering & Materials Science, 5050 Anthony Wayne Drive, Room 1115, College of Engineering, Wayne State University, Detroit, MI 48202, USA
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Interests: plant-wide control of nonlinear systems; centralized and distributed economic model predictive control; operational safety of closed-loop processes; reduced-order modeling in predictive control
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.
Prof. Dr. Jinfeng Liu
Prof. Dr. Helen E Durand
Manuscript Submission Information
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- Optimal control
- Predictive control
- Receding horizon control
- Moving horizon estimation
- Nonlinear programming
- Distributed estimation