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Model Predictive Control
About this book
Model Predictive Control
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.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- About the Authors
- Preface
- Acronyms
- Introduction
- 1 Concepts
- 2 Parameter Estimation and Output Prediction
- 3 SteadyâState Target Calculation
- 4 TwoâLayered DMC for Stable Processes
- 5 TwoâLayered DMC for Stable and Integral Processes
- 6 TwoâLayered DMC for StateâSpace Model
- 7 OffsetâFree, Nonlinearity and Variable Structure in TwoâLayered MPC
- 8 TwoâStep Model Predictive Control for Hammerstein Model
- 9 Heuristic Model Predictive Control for LPV Model
- 10 Robust Model Predictive Control
- 11 Output Feedback Robust Model Predictive Control
- References
- Index
- End User License Agreement