Model Predictive Control: Within a Two-Layered Framework (IEEE Press)

Understand the practical side of controlling industrial processes

Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings.

Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book’s title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace.

Model Predictive Control readers will also find:

• Two-part organization to balance theory and applications
• Selection of topics directly driven by industrial demand
• An author with decades of experience in both teaching and industrial practice

This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.

Baocang Ding, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, control of power network, process control, and control software development.

Yuanqing Yang, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, fuzzy control, networked control, and distributed control systems.